RESPONSIBILITIES:<br>ML Model Deployment & Platform Management<br>• Lead the design, implementation, and ongoing maintenance of scalable ML infrastructure on Databricks, including ML flow for experiment tracking, model registry, and model serving endpoints.<br>• Oversee the development of the ML Ops platform and automated pipelines for deploying, monitoring, and maintaining models within production environments.<br>• Implement robust solutions for model versioning, systematic retraining, and comprehensive artifact management using Databricks Unity Catalog for ML governance.<br>• Design and manage Databricks Feature Store for consistent feature engineering across training and inference pipelines.<br>Generative AI & LLM Operations<br>• Architect and implement Retrieval-Augmented Generation (RAG) systems for document Q&A, enabling business teams to query fund documents, investor letters, and market research.<br>• Design, deploy, and manage vector database solutions (Databricks Vector Search, Pinecone, or similar) for semantic search and retrieval across enterprise documents.<br>• Lead LLM fine-tuning and customization initiatives, training models like Claude or open-source alternatives with CIM proprietary data while ensuring data privacy and compliance.<br>• Develop and optimize document processing pipelines including PDF parsing, chunking strategies, and embedding generation for RAG applications.<br>• Implement prompt engineering best practices and LLM evaluation frameworks to ensure output quality, relevance, and factual accuracy.<br>• Build guardrails and safety measures for GenAI applications, including hallucination detection, output validation, and source attribution.<br>Automation & CI/CD Pipelines<br>• Design and implement extensive automation across the ML workflow, covering model training, testing, validation, and deployment using Databricks Workflows and Asset Bundles.<br>• Set up robust CI/CD pipelines for both traditional ML models and GenAI applications, leveraging GitHub Actions, Azure DevOps, or similar tools.<br>• Automate complex data and model workflows utilizing orchestration tools such as Airflow, Prefect, or Databricks Workflows.
<p><strong>Machine Learning Engineer</strong></p><p><br></p><p><strong>Company Overview</strong></p><p>Based in Los Angeles, California, the company specializes in transforming complex, multi-source data into actionable insights through machine learning, knowledge graph technologies, and advanced analytics. This is an opportunity to work on mission-critical applications in a highly collaborative environment focused on innovation, scalability, and operational excellence.</p><p><br></p><p><strong>Role Summary</strong></p><p>The Machine Learning Engineer will play a critical role in designing, training, deploying, and optimizing machine learning models that operate on large-scale temporal, geospatial, relational, and unstructured datasets. This position requires an experienced engineer who can independently own the full machine learning lifecycle, from dataset development and model architecture selection to deployment, monitoring, and continuous improvement. The ideal candidate brings broad expertise across computer vision, natural language processing (NLP), geospatial analytics, MLOps, and large-scale production machine learning environments.</p><p><br></p><p><strong>Key Responsibilities</strong></p><ul><li>Design, train, evaluate, deploy, and optimize machine learning models across multiple production use cases.</li><li>Build predictive solutions for anomaly detection, forecasting, entity resolution, relationship prediction, risk assessment, and operational decision support.</li><li>Partner with data engineering teams to develop high-quality training datasets from structured, unstructured, temporal, relational, and geospatial data sources.</li><li>Design model architectures and select appropriate algorithms based on business objectives, data characteristics, and operational requirements.</li><li>Develop and maintain machine learning pipelines spanning data preparation, feature engineering, training, evaluation, deployment, and monitoring.</li><li>Build scalable solutions that leverage graph-based and knowledge graph-driven data architectures.</li><li>Develop models utilizing computer vision, NLP, geospatial analytics, and predictive modeling techniques.</li><li>Establish rigorous evaluation frameworks, baselines, performance metrics, and validation methodologies.</li><li>Design experiments that mitigate data leakage, model drift, bias, and changing data distributions.</li><li>Implement monitoring, observability, alerting, retraining, rollback, and model governance processes.</li><li>Maintain reproducible datasets, model artifacts, evaluation results, and deployment workflows.</li><li>Collaborate with distributed engineering teams to deliver reliable and scalable machine learning capabilities.</li><li>Improve model calibration, confidence scoring, uncertainty estimation, and explainability.</li><li>Contribute to technical architecture, machine learning standards, and long-term platform strategy.</li></ul><p><strong>Additional Details</strong></p><ul><li>Fully onsite 5 days a week</li><li>Highly collaborative environment with strong emphasis on machine learning, knowledge graphs, and data-driven decision support</li><li>Opportunity to influence technical direction, machine learning standards, and model lifecycle practices across multiple initiatives</li><li>Candidates must be authorized to work in the United States and satisfy applicable regulatory employment requirements</li></ul>
<p><strong>Machine Learning Engineer</strong></p><p><br></p><p><strong>Company Overview</strong></p><p>Based in Los Angeles, California, the company specializes in transforming complex, multi-source data into actionable insights through machine learning, knowledge graph technologies, and advanced analytics. This is an opportunity to work on mission-critical applications in a highly collaborative environment focused on innovation, scalability, and operational excellence.</p><p><br></p><p><strong>Role Summary</strong></p><p>The Machine Learning Engineer will play a critical role in designing, training, deploying, and optimizing machine learning models that operate on large-scale temporal, geospatial, relational, and unstructured datasets. This position requires an experienced engineer who can independently own the full machine learning lifecycle, from dataset development and model architecture selection to deployment, monitoring, and continuous improvement. The ideal candidate brings broad expertise across computer vision, natural language processing (NLP), geospatial analytics, MLOps, and large-scale production machine learning environments.</p><p><br></p><p><strong>Key Responsibilities</strong></p><ul><li>Design, train, evaluate, deploy, and optimize machine learning models across multiple production use cases.</li><li>Build predictive solutions for anomaly detection, forecasting, entity resolution, relationship prediction, risk assessment, and operational decision support.</li><li>Partner with data engineering teams to develop high-quality training datasets from structured, unstructured, temporal, relational, and geospatial data sources.</li><li>Design model architectures and select appropriate algorithms based on business objectives, data characteristics, and operational requirements.</li><li>Develop and maintain machine learning pipelines spanning data preparation, feature engineering, training, evaluation, deployment, and monitoring.</li><li>Build scalable solutions that leverage graph-based and knowledge graph-driven data architectures.</li><li>Develop models utilizing computer vision, NLP, geospatial analytics, and predictive modeling techniques.</li><li>Establish rigorous evaluation frameworks, baselines, performance metrics, and validation methodologies.</li><li>Design experiments that mitigate data leakage, model drift, bias, and changing data distributions.</li><li>Implement monitoring, observability, alerting, retraining, rollback, and model governance processes.</li><li>Maintain reproducible datasets, model artifacts, evaluation results, and deployment workflows.</li><li>Collaborate with distributed engineering teams to deliver reliable and scalable machine learning capabilities.</li><li>Improve model calibration, confidence scoring, uncertainty estimation, and explainability.</li><li>Contribute to technical architecture, machine learning standards, and long-term platform strategy.</li></ul><p><strong>Additional Details</strong></p><ul><li>Fully onsite 5 days a week</li><li>Highly collaborative environment with strong emphasis on machine learning, knowledge graphs, and data-driven decision support</li><li>Opportunity to influence technical direction, machine learning standards, and model lifecycle practices across multiple initiatives</li><li>Candidates must be authorized to work in the United States and satisfy applicable regulatory employment requirements</li></ul>
<p>We are seeking a skilled AI Engineer to join our dynamic technology team. The ideal candidate has hands-on experience integrating advanced AI and large language model (LLM) features into applications, as well as a strong background in designing and delivering AI-driven solutions. In this role, you will work closely with product, engineering, and data teams to build and enhance innovative products using the latest AI frameworks and tools.</p><p><br></p><p><strong>Key Responsibilities:</strong></p><p><br></p><ul><li>Design, develop, and integrate AI and LLM features into new or existing applications, ensuring scalable and reliable deployment.</li><li>Collaborate with cross-functional teams to define technical requirements and deliver AI-driven functionalities in production environments.</li><li>Utilize AI frameworks, APIs, and platforms such as OpenAI, LangChain, vector databases, and machine learning libraries to accelerate solution development.</li><li>Lead prompt engineering, fine-tuning, and model optimization initiatives to improve performance and user outcomes.</li><li>Evaluate and select the most appropriate AI/ML models, tools, and platforms for project needs.</li><li>Conduct documentation, code reviews, testing, and performance monitoring of AI-driven products.</li><li>Stay up to date with advancements in artificial intelligence, generative models, and industry best practices.</li></ul><p><br></p>
We are looking for an Artificial Intelligence (AI) Engineer to help build and expand practical AI capabilities for the business. This role works closely with functional leaders to uncover meaningful use cases, convert operational challenges into scalable technical solutions, and deliver tools that improve decision-making and efficiency. It is a strong opportunity for a hands-on specialist who enjoys working in a developing environment and wants to influence how AI is applied across the organization.<br><br>Responsibilities:<br>• Partner with business leaders to understand operational pain points and identify where AI can create measurable value.<br>• Translate business needs into solution concepts, technical approaches, and implementable AI initiatives.<br>• Design, develop, test, and deploy AI and machine learning applications that address real-world business problems.<br>• Evaluate repetitive workflows and manual tasks across departments to recommend automation or intelligent assistance opportunities.<br>• Collaborate with teams such as finance, recruiting, and operations to create tools that improve productivity and information flow.<br>• Help establish an internal pipeline of AI initiatives by assessing impact, feasibility, and implementation priorities.<br>• Serve as a bridge between non-technical stakeholders and technical execution by clearly communicating options, risks, and outcomes.<br>• Contribute to the growth of the organization’s AI capability through hands-on development, experimentation, and continuous improvement.
We are looking for an experienced Artificial Intelligence (AI) Engineer to create practical, scalable AI solutions that help improve business performance and streamline operations in Brookfield, Wisconsin. In this role, you will partner with technical teams and business leaders to turn complex challenges into intelligent applications, machine learning systems, and generative AI capabilities. The ideal candidate brings a strong software engineering foundation along with hands-on experience delivering AI solutions in cloud environments.<br><br>Responsibilities:<br>• Build and implement AI, machine learning, and generative AI solutions that address business and operational needs.<br>• Develop intelligent applications such as virtual assistants, chatbot experiences, copilots, and automated workflows.<br>• Train, evaluate, and refine machine learning models to improve performance, reliability, and business impact.<br>• Create and support retrieval-augmented generation solutions and related applications for knowledge-driven use cases.<br>• Work closely with stakeholders to uncover opportunities where AI can enhance processes, decision-making, and user experiences.<br>• Connect AI capabilities with enterprise systems and cloud-based platforms to enable practical adoption at scale.<br>• Establish model deployment and support practices, including monitoring, governance, and lifecycle management.<br>• Apply security, compliance, and responsible AI standards throughout solution design, development, and release.<br>• Research new AI tools and technologies and recommend options that align with organizational goals.<br>• Produce clear technical documentation covering solution architecture, workflows, and implementation details.
AI/LLM Engineer / Solutions Architect - no 3rd parties, no C2C - no must be eligible to work in the U.S. - Remote with 2 weeks onsite to onboard We are looking for an Artificial Intelligence (AI) Engineer to lead practical AI development efforts for a manufacturing organization in Lincolnton, North Carolina. This Long-term Contract position will focus on creating production-ready automation tools and intelligent workflows that improve decision-making, streamline operations, and uncover business insights from enterprise data. The role is best suited for someone who can translate complex operational needs into scalable AI solutions while partnering closely with cross-functional teams such as Finance and Sales. <br> Responsibilities: • Design and deploy AI-powered applications that automate recurring operational activities and improve workflow efficiency across the business. • Build intelligent solutions that analyze enterprise and operational data to identify trends, risks, and opportunities that support growth and performance. • Create tools that provide visibility into customer purchasing behavior, inventory conditions, material planning, and seasonal business patterns. • Develop AI-driven capabilities that help teams investigate operational issues, uncover contributing factors, and support faster resolution. • Integrate AI models and services with existing business platforms, data sources, and external systems through APIs and structured pipelines. • Partner with stakeholders in Finance, Sales, and other functional areas to define use cases, prioritize deliverables, and align solutions with business goals. • Establish practical standards for AI implementation, including solution scope, governance, and responsible deployment practices. • Produce market intelligence solutions that monitor external signals such as public-sector funding activity, bid opportunities, and competitor developments. • Participate in early onsite collaboration to understand the manufacturing environment, team workflows, and operational priorities before broader delivery begins.
We are looking for an Artificial Intelligence (AI) Engineer to create practical solutions that improve how teams work, analyze information, and make decisions. This position is based in Jupiter, Florida, and focuses on designing intelligent applications, workflow automations, and data-driven tools that support business and financial operations. You will collaborate with stakeholders across the organization to translate open-ended challenges into reliable, scalable products built with modern engineering and cloud technologies.<br><br>Responsibilities:<br>• Design and develop internal AI-powered applications, dashboards, and automation tools that streamline day-to-day business activities.<br>• Integrate APIs, databases, and core business platforms to reduce manual effort and improve process efficiency.<br>• Create reporting, research, and analytical workflows that use AI capabilities to deliver faster and more useful insights.<br>• Build solutions that assist with investment analysis, portfolio oversight, and broader financial decision support.<br>• Automate recurring deliverables across finance, accounting, and operational reporting functions.<br>• Develop and maintain solutions within Microsoft 365, Azure, GitHub, and related technology environments.<br>• Define engineering best practices by establishing documentation, reusable standards, and repeatable development processes.<br>• Assess existing technology architecture, system integrations, access controls, and security considerations to support reliable implementation.<br>• Partner closely with business users and leadership to understand needs, prioritize opportunities, and deliver functional tools that solve real problems.
<p>Robert Half is hiring for a Principal level Software Engineer to focus on agentic workflows and AI software products. In this role, you will lead full product development efforts while creating intelligent, scalable applications that combine agentic AI capabilities with modern web technologies. The position calls for a hands-on engineer who can build reliable backend systems, contribute to user-facing experiences, and partner across teams to deliver secure, high-performing solutions.</p><p><br></p><p>Responsibilities:</p><p>• Lead the full software development lifecycle for AI-enabled products, from technical planning and architecture through deployment and ongoing improvement.</p><p>• Create and refine agentic AI workflows that support business objectives and integrate effectively with application services.</p><p>• Build, test, and support scalable server-side applications using Python frameworks such as Django, Flask, or FastAPI, or Node.js frameworks including Express or NestJS.</p><p>• Develop and maintain RESTful or GraphQL interfaces that enable dependable communication between applications and services.</p><p>• Contribute to front-end development with React, ensuring smooth interaction between user interfaces and backend functionality.</p><p>• Architect and support microservices-based and distributed systems designed for reliability, flexibility, and growth.</p><p>• Improve overall system efficiency by tuning application performance, refining database access patterns, and managing cloud resource consumption.</p><p>• Apply strong security practices by implementing access control, authentication, and authorization measures across applications.</p><p>• Oversee containerized delivery processes and automated deployment workflows using Docker and CI/CD tools, while diagnosing and resolving production issues in cloud environments.</p><p>• Work closely with product, design, and DevOps partners to deliver maintainable, well-documented solutions that meet project goals.</p>
<p>We are looking for an Artificial Intelligence (AI) Engineer to support the design, deployment, and ongoing operation of AI systems for a government services organization in Albuquerque, New Mexico. This position centers on building reliable AI infrastructure, enabling machine learning solutions in production, and partnering with cross-functional teams to deliver secure, scalable platforms. </p><p>The ideal candidate brings strong experience in automation, Kubernetes-based deployments, and modern MLOps practices, along with the ability to translate technical needs into durable operational solutions.</p><p><br></p><p>Responsibilities:</p><p>• Direct the rollout and integration of AI platforms and services, ensuring they work effectively with existing enterprise technologies and operational standards.</p><p>• Architect, implement, and refine AI infrastructure in partnership with cloud, server, and platform engineering teams to support dependable system performance.</p><p>• Move machine learning solutions from development into production by establishing repeatable processes for deployment, maintenance, and long-term support.</p><p>• Create and manage CI/CD and MLOps workflows that cover model validation, packaging, release, rollback, and lifecycle oversight.</p><p>• Automate infrastructure and platform operations through scripting, infrastructure-as-code methods, and configuration management tools.</p><p>• Troubleshoot platform and service issues, perform root cause analysis, and produce clear technical documentation for support and maintenance activities.</p><p>• Strengthen system visibility by implementing logging, monitoring, alerting, and incident response practices across AI environments.</p><p>• Uphold security and compliance expectations by contributing to audits, remediation efforts, vulnerability management, and secure design reviews.</p><p>• Identify and deliver improvements that increase performance, scalability, reliability, and cost efficiency across AI-enabled systems.</p><p>• Work with technical and business stakeholders to align AI implementations with organizational priorities and evaluate emerging tools for long-term operational value.</p><p>Other duties as needed</p>
<p><strong>Data & Knowledge Engineer</strong></p><p><br></p><p><strong>Company Overview</strong></p><p>A leading artificial intelligence and advanced analytics organization is seeking a Data & Knowledge Engineer to help power next-generation AI and decision-support platforms. Based in Los Angeles, California, the company specializes in integrating complex data from disparate sources into unified intelligence systems that support advanced analytics, automation, and operational decision-making. This is an opportunity to work on mission-critical initiatives involving large-scale data, knowledge graphs, and AI-driven applications.</p><p><br></p><p><strong>Role Summary</strong></p><p>The Data & Knowledge Engineer will lead the onboarding, transformation, and governance of complex multimodal data into scalable data and knowledge platforms. This role focuses on integrating structured and unstructured data sources, designing reusable data pipelines, developing entity resolution frameworks, and enabling high-quality data for analytics, retrieval, AI workflows, and geospatial applications. The ideal candidate combines strong data engineering expertise with experience in knowledge graphs, data quality, and large-scale information management.</p><p><br></p><p><strong>Key Responsibilities</strong></p><ul><li>Design and develop scalable data ingestion pipelines for structured, unstructured, geospatial, and sensor-based data sources.</li><li>Build and maintain batch and streaming data processing systems across cloud, on-premises, and disconnected environments.</li><li>Develop integrations with APIs, databases, file systems, enterprise applications, and external data sources.</li><li>Design schema mapping, normalization, and transformation processes that support diverse customer data models.</li><li>Implement entity resolution, record linkage, deduplication, and data matching capabilities across multiple sources.</li><li>Preserve data lineage, provenance, auditing, and traceability throughout the data lifecycle.</li><li>Create data validation, monitoring, replay, and exception-handling processes for complex data environments.</li><li>Develop workflows for managing ambiguous records, conflicting information, and data quality issues.</li><li>Define and measure data quality metrics, onboarding effectiveness, and operational performance indicators.</li><li>Support knowledge graph, retrieval, AI, and analytics capabilities through high-quality governed datasets.</li><li>Partner with engineering and stakeholder teams to transform recurring onboarding requirements into reusable platform capabilities.</li><li>Contribute to platform architecture, engineering standards, and long-term data strategy initiatives.</li></ul><p><strong>Additional Details</strong></p><ul><li>Fully onsite 5 days a week</li><li>Full-time exempt position</li><li>Hands-on engineering role with substantial ownership and technical influence</li><li>Opportunity to work on large-scale data, knowledge graph, and AI-driven initiatives</li><li>Staff-level candidates may provide architectural leadership, mentorship, and engineering guidance</li><li>Candidates must be authorized to work in the United States and satisfy applicable regulatory employment requirements</li></ul>
<p>Our client is seeking a highly technical and innovative AI Solutions Engineer to design, build, and maintain internal applications, automations, and AI-powered tools that improve operational efficiency across the organization. This is not a traditional product development role. Instead, this individual will focus on solving business challenges through custom software development, workflow automation, system integrations, and practical applications of artificial intelligence.</p><p>The ideal candidate thrives in fast-paced environments, enjoys transforming ambiguous business needs into working solutions, and is comfortable partnering with both technical and non-technical stakeholders. This position offers the opportunity to work on a wide variety of projects while leveraging modern software development practices, cloud technologies, and AI capabilities to drive operational excellence.</p><p>Key Responsibilities</p><ul><li>Design, develop, and support internal applications, dashboards, automation tools, and workflow solutions that enhance business operations.</li><li>Build and integrate AI and Large Language Model (LLM) capabilities into existing processes, including document analysis, classification, workflow routing, content generation, summarization, and reporting.</li><li>Develop and maintain integrations between internal systems and third-party platforms using APIs and web services.</li><li>Rapidly prototype new solutions, gather business feedback, and evolve prototypes into scalable, maintainable production applications.</li><li>Collaborate with cross-functional teams to identify process improvement opportunities and implement technology-driven solutions.</li><li>Ensure applications meet security, privacy, governance, and compliance requirements.</li><li>Create and maintain technical documentation, architecture diagrams, and operational procedures to ensure ongoing supportability and audit readiness.</li><li>Monitor, troubleshoot, and enhance deployed solutions to ensure reliability and performance.</li></ul><p><br></p>
<p><strong>Software Automation Engineer IV</strong></p><p><strong>Location: </strong>Remote, East Coast hours</p><p><br></p><p><strong>Software Automation Engineer</strong> to support the incident response SOC (Cyber Security Operations Center – CSOC) that will focus on building (design and develop) and implementing AI driven automation solutions through AI agents, AI powered workflows, and process automation to reduce repetitive work and enabling security teams to focus on high value investigations. </p><p><br></p><p><strong><u>Responsibilities:</u></strong></p><ul><li>Partner with various SOC Leaders, technical owners, and stakeholders to understand operational challenges and automation opportunities.</li><li>Design, build, and deploy AI-powered solutions such as: </li><li>AI triage bots</li><li>Watchlist automation</li><li>Incident response automation</li><li>Security operations workflows</li><li>AI agents and intelligent automation tools</li><li>Build solutions end-to-end, from requirements gathering through deployment and support.</li><li>Participate in architectural design and technical decision-making.</li><li>Integrate AI and automation tools into existing enterprise systems and workflows.</li><li>Improve SOC efficiency by reducing false positives, manual effort, and administrative tasks.</li><li>Develop software solutions utilizing AWS services and APIs.</li><li>Collaborate across security, engineering, and operations teams.</li></ul>
<p>We are looking for a skilled Data Engineer to join a 100% remote contract to hire position. This role focuses on developing and maintaining data warehouse integration processes, working closely with technical teams and business stakeholders to deliver reliable, high-quality data solutions. The ideal candidate brings deep experience in data transformation, warehouse architecture, and production support, along with a proactive approach to investigating and resolving complex data issues.</p><p><br></p><p>Responsibilities:</p><p>• Build, enhance, and maintain data loading and transformation workflows that feed enterprise data warehouse environments and related systems.</p><p>• Create scalable warehouse integration solutions while producing clear technical documentation and following established engineering standards.</p><p>• Contribute to data modeling efforts and collaborate with cross-functional teams on reporting structures and warehouse design decisions.</p><p>• Partner with business stakeholders to understand operational needs and translate them into effective technical approaches and implementation plans.</p><p>• Review solution quality through testing, validation, and design assessments to ensure dependable performance and efficient processing.</p><p>• Monitor production data warehouse operations, investigate pipeline failures or data discrepancies, and resolve issues with urgency to reduce business disruption.</p><p>• Work closely with development, quality assurance, and support teams to help deliver solutions on schedule across the full development lifecycle.</p><p>• Raise risks, communicate technical concerns, and provide input on specifications to improve solution accuracy and delivery outcomes.</p>
<p>We are looking for a Data Engineer to support data-focused initiatives with a strong emphasis on controls, process clarity, and technical documentation. This is a Long-term Contract position expected to begin as a 3-4 month engagement at 40 hours per week, with remote work flexibility. The ideal candidate will help strengthen data workflows, improve reliability across engineering processes, and create well-organized documentation that supports ongoing delivery and compliance.</p><p><br></p><p>Responsibilities:</p><p>• Design, build, and maintain data pipelines that support reliable movement and transformation of information across platforms.</p><p>• Develop clear technical documentation, data process records, and control-related artifacts to improve transparency and audit readiness.</p><p>• Use Python, Apache Spark, and ETL frameworks to prepare, cleanse, and transform large datasets for downstream consumption.</p><p>• Work with Hadoop- and Kafka-based environments to support scalable data processing and streaming or batch integration needs.</p><p>• Review existing data workflows to identify gaps in controls, consistency, and documentation quality, then recommend practical improvements.</p><p>• Collaborate with cross-functional stakeholders to clarify data requirements, align engineering deliverables, and support operational continuity.</p><p>• Monitor pipeline performance and troubleshoot data issues to maintain dependable processing and accurate outputs.</p>
<p>We are looking for a senior Data Engineer. This position combines architectural leadership with hands-on development across a modern cloud data ecosystem, with a strong focus on building dependable, scalable, and efficient data solutions. The role will help shape engineering standards, strengthen platform governance, and support high-quality data delivery for enterprise analytics and emerging AI-driven use cases.</p><p><br></p><p>Responsibilities:</p><p>• Provide technical direction to engineering teams and guide the successful execution of complex data platform initiatives.</p><p>• Create and enhance robust data pipelines and reusable data products that support scalable analytics and reporting needs.</p><p>• Develop and refine cloud-based data solutions using Snowflake, dbt, Fivetran, and Azure services.</p><p>• Build automation-first, metadata-driven processing frameworks to improve consistency, maintainability, and operational efficiency.</p><p>• Design curated data structures using established modeling approaches such as Data Vault, Kimball, and dimensional modeling.</p><p>• Improve platform performance through tuning, cost management, and scalability planning across data workloads.</p><p>• Establish and reinforce best practices for data quality, governance, lineage tracking, observability, and CI/CD delivery.</p><p>• Support Azure-based integration, storage, compute, and DevOps capabilities required for reliable end-to-end data operations.</p><p>• Enable data foundations that can support advanced analytics, AI/ML initiatives, and modern analytical architectures.</p>
We are looking for a Senior Data Engineer to develop and optimize enterprise data systems that support analytics and digital solutions. In this role, you will design and implement robust data architectures, ensuring seamless data integration and transformation processes across the organization. Your expertise will drive the creation of reliable pipelines and scalable infrastructure, enabling advanced analytics and machine learning capabilities.<br><br>Responsibilities:<br>• Design and implement scalable data pipelines using Databricks, Spark, and Delta Lake to support enterprise-level analytics.<br>• Develop and maintain efficient data models tailored for AI, analytics, and operational systems.<br>• Lead Master Data Management initiatives to establish unified and accurate data records across platforms.<br>• Create batch and near-real-time data processing workflows for structured and semi-structured datasets.<br>• Collaborate with AI and software development teams to ensure delivery of high-quality datasets for machine learning.<br>• Define and enforce data architecture standards, ensuring scalability, reliability, and governance.<br>• Troubleshoot and optimize data systems to maintain performance and reliability in complex environments.<br>• Partner with cloud and IT teams to integrate modern data platforms and ensure seamless functionality.
<p>We are looking for an experienced Data Engineer to jcreate and enhance dependable data platforms that support HR and enterprise analytics, partnering closely with analysts, engineering leads, and business stakeholders on site. The position focuses on building scalable pipelines, strengthening data quality, and enabling trusted insights that improve operational decision-making across the organization.</p><p><br></p><p>Responsibilities:</p><p>• Design, develop, and maintain scalable data pipelines that integrate information from files, APIs, databases, replicated sources, and streaming inputs.</p><p>• Build and support modern data environments across warehouses, data lakes, and lakehouse architectures to meet analytics and reporting needs.</p><p>• Partner with business analysts, HR stakeholders, and technical team members to translate data requirements into reliable engineering solutions.</p><p>• Improve data quality, lineage, and governance by applying metadata-driven practices and implementing controls that increase trust in enterprise datasets.</p><p>• Lead end-to-end delivery of data engineering initiatives, from solution design and development through testing, deployment, and operational support.</p><p>• Manage orchestration, scheduling, and monitoring of data workflows to ensure stable performance and timely delivery of critical datasets.</p><p>• Apply DevOps practices such as version control, automated testing, and CI/CD processes to increase deployment quality and team collaboration.</p><p>• Use Python, SQL, and cloud-based tools to automate data processing, optimize performance, and support scalable distributed workloads.</p><p>• Implement data protection measures, including masking, encryption, anonymization, and role-aware access design, especially for sensitive workforce information.</p><p>• Support curated analytical datasets and reporting solutions by collaborating with downstream users on trusted models and enterprise data products.</p>
We are looking for a Data Engineer to help design and enhance data solutions that support reliable reporting and analytics in Salt Lake City, Utah. This role focuses on building scalable data pipelines, shaping well-structured warehouse models, and improving the quality and usability of enterprise data assets. The ideal candidate brings strong experience with SQL, Python, AWS technologies, and dimensional modeling principles grounded in the Kimball methodology.<br><br>Responsibilities:<br>• Build and maintain data pipelines that ingest, transform, and prepare information for downstream analytics and business use.<br>• Design warehouse structures using Kimball-based dimensional modeling practices to support clear, consistent reporting.<br>• Develop and optimize SQL- and Python-driven data workflows with an emphasis on scalability, performance, and maintainability.<br>• Partner with analysts, engineers, and business stakeholders to translate data needs into practical engineering solutions.<br>• Create and manage data transformation logic using dbt to improve testing, documentation, and deployment consistency.<br>• Support cloud-based data platforms in an AWS environment and contribute to ongoing improvements in data architecture.<br>• Monitor data quality and troubleshoot pipeline issues to ensure dependable delivery of curated datasets.<br>• Contribute to modern data engineering practices, including tools such as PySpark when needed for larger-scale processing.
<p>We are looking for a Data Engineer to build and enhance reporting and data solutions that help teams make informed business decisions across the organization. This role partners with groups such as asset management, acquisitions, accounting, and HR to translate business needs into scalable dashboards, reliable data pipelines, and actionable insights. Based in Los Angeles, California, the position is ideal for someone who combines strong technical depth with the ability to explain findings clearly to a range of stakeholders.</p><p><br></p><p>Responsibilities:</p><p>• Create and refine dashboards, reports, and automated data outputs using tools such as Python, Excel, and Power BI to support operational and strategic reporting needs.</p><p>• Examine large and varied datasets to uncover meaningful trends, performance indicators, and opportunities for improvement.</p><p>• Guide and support entry-level BI team members by reviewing work, sharing best practices, and contributing to their career growth.</p><p>• Partner with stakeholders across multiple business functions to define reporting goals, gather technical requirements, and deliver effective data solutions.</p><p>• Translate technical concepts and analytical results into clear recommendations for non-technical audiences.</p><p>• Contribute to cross-department initiatives that improve data availability, reporting consistency, and overall usability of business information.</p><p>• Develop and maintain queries, procedures, and data workflows to support extraction, transformation, and loading activities across reporting environments.</p><p>• Monitor data quality through validation checks, issue resolution, and routine audits to maintain accuracy and reliability.</p><p>• Administer BI platforms with attention to performance, access control, and system stability while assisting users with adoption and training.</p><p><br></p><p>For immediate consideration, apply now and direct message Reid Gormly on LinkedIn</p>
We are looking for a Data Engineer to join our team in Jacksonville, Florida and help shape scalable, enterprise-ready analytics solutions. In this role, you will turn complex business needs into well-structured data models, high-performing pipelines, and actionable reporting assets that support informed decision-making. The ideal candidate brings deep experience with modern data platforms, strong technical leadership, and the ability to guide best practices for analytics, governance, and data usability across the organization.<br><br>Responsibilities:<br>• Design and maintain scalable data pipelines and transformation workflows using tools such as Python, Apache Spark, Hadoop, Kafka, and ETL frameworks.<br>• Build and optimize enterprise semantic models that support consistent metrics, reusable analytics assets, and reliable reporting across multiple business areas.<br>• Partner with business and technical stakeholders to convert complex requirements into practical data solutions, dashboards, and analytical datasets.<br>• Develop and support analytics solutions within modern cloud and hybrid environments, with a strong focus on Microsoft Power BI, Microsoft Fabric, and Azure Synapse Analytics.<br>• Improve data quality, model performance, and platform reliability through testing, validation, tuning, and adherence to established governance standards.<br>• Provide technical guidance to team members by sharing best practices in data modeling, visualization, and scalable analytics design.<br>• Evaluate and incorporate AI-enabled analytics capabilities and automation opportunities while ensuring outputs align with business needs and quality expectations.<br>• Communicate technical concepts and analytical insights clearly to a range of audiences, including leadership, to support strategic decisions.
We are looking for a Data Engineer to help build reliable data solutions that support reporting, analytics, and day-to-day business decisions in Princeton, New Jersey. This position focuses on creating scalable data flows, improving platform performance, and organizing data for efficient access across teams. The ideal candidate brings strong technical depth in pipeline development, database technologies, and cloud-based data environments.<br><br>Responsibilities:<br>• Build, enhance, and support scalable ETL workflows that move and transform data from multiple sources into trusted analytical platforms.<br>• Create and refine data models, storage structures, and warehouse solutions to improve accessibility, efficiency, and long-term maintainability.<br>• Monitor data processes to identify issues, strengthen reliability, and maintain high standards for accuracy, consistency, and performance.<br>• Partner with analytics, engineering, and business stakeholders to understand data needs and deliver practical data solutions.<br>• Optimize SQL Server and related data systems to support efficient querying, integration, and operational stability.<br>• Implement and maintain orchestration processes using tools such as Airflow or comparable workflow technologies.<br>• Contribute to cloud-based data infrastructure initiatives across platforms such as Azure and other enterprise cloud environments.
<p>Robert Half is seeking a <strong>Contract Data Engineer</strong> to support our client’s data and analytics initiatives. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines and infrastructure that enable efficient data ingestion, transformation, and delivery. The ideal candidate has strong experience working with modern data platforms, cloud environments, and large-scale datasets.</p><p><br></p><p><strong>Key Responsibilities:</strong></p><ul><li><strong>Data Pipeline Development:</strong> Design, build, and maintain scalable ETL / ELT pipelines to ingest, transform, and deliver data from multiple sources.</li><li><strong>Data Architecture:</strong> Develop and optimize data models, schemas, and warehouse structures to support analytics, reporting, and business intelligence needs.</li><li><strong>Cloud Data Platforms:</strong> Work within cloud environments such as <strong>AWS, Azure, or GCP</strong> to deploy and manage data solutions.</li><li><strong>Data Warehousing:</strong> Design and support enterprise data warehouses using platforms such as <strong>Snowflake, Redshift, BigQuery, or Azure Synapse</strong>.</li><li><strong>Big Data Processing:</strong> Develop solutions using big data technologies such as <strong>Spark, Databricks, Kafka, and Hadoop</strong> when required.</li><li><strong>Performance Optimization:</strong> Tune queries, pipelines, and storage solutions for performance, scalability, and cost efficiency.</li><li><strong>Data Quality & Reliability:</strong> Implement monitoring, validation, and alerting processes to ensure data accuracy, integrity, and availability.</li><li><strong>Collaboration:</strong> Work closely with Data Analysts, Data Scientists, Software Engineers, and business stakeholders to understand requirements and deliver data solutions.</li><li><strong>Documentation:</strong> Maintain detailed documentation for pipelines, data flows, and system architecture.</li></ul><p><br></p>
We are looking for a Data Engineer to help maintain and improve a mission-driven technology environment that supports essential nonprofit operations in Battle Creek, Michigan. This position blends application support, database engineering, and systems integration work, making it ideal for someone who enjoys solving technical problems across multiple platforms. The role works closely with internal teams and external partners to keep business systems reliable, data accurate, and reporting processes running smoothly.<br><br>Responsibilities:<br>• Maintain and resolve issues across core business applications, including Dynamics 365 Business Central, Microsoft 365, SharePoint, Power BI, procurement tools, workforce systems, and other connected platforms.<br>• Administer Microsoft SQL Server environments by monitoring system health, tuning performance, managing security access, and overseeing backup, recovery, upgrade, and capacity planning activities.<br>• Build, refine, and troubleshoot SQL queries to support operational needs while identifying and correcting data consistency, accuracy, and reconciliation problems between systems.<br>• Develop and support integrations, APIs, ETL processes, Azure Data Factory workflows, scheduled data exchanges, and SFTP-based transfers to ensure dependable movement of information across platforms.<br>• Investigate reporting and data warehouse issues by resolving failed loads, refresh interruptions, and mismatches between source systems and business intelligence outputs.<br>• Contribute to software rollouts and system enhancements through testing, data conversion support, deployment activities, post-launch issue resolution, and production stabilization efforts.<br>• Partner with software vendors and external service providers to diagnose complex technical challenges and drive incidents through to completion.<br>• Create and maintain clear technical documentation for applications, databases, integrations, and data flows while sharing knowledge with teammates to strengthen team coverage.<br>• Support both strategic engineering work and day-to-day technical tasks within a collaborative IT team, adapting to changing priorities as needed.
We are looking for a Data Engineer to build and enhance scalable data solutions that support analytics and business decision-making in Jacksonville, Florida. This role focuses on designing reliable warehouse structures, developing efficient data pipelines, and improving data quality across enterprise platforms. The ideal candidate brings strong technical depth in modern data engineering practices and is comfortable partnering with cross-functional teams to deliver well-governed, high-performing data assets.<br><br>Responsibilities:<br>• Design and maintain enterprise data warehouse solutions using dimensional modeling techniques that support reporting, analytics, and long-term scalability.<br>• Develop, orchestrate, and optimize ETL and ELT workflows for batch and near real-time data movement using modern pipeline and scheduling tools.<br>• Build robust data architectures across cloud-based platforms while balancing performance, resilience, and cost efficiency.<br>• Troubleshoot pipeline failures, data inconsistencies, and performance bottlenecks, then implement practical fixes that improve reliability and throughput.<br>• Apply governance standards by supporting data quality controls, metadata practices, lineage visibility, and role-based access management.<br>• Partner with business and technical stakeholders to translate data needs into well-structured solutions and provide clear updates on progress, risks, and delivery timelines.<br>• Create efficient code for data transformation and aggregation using Python and advanced query development techniques.<br>• Integrate data from databases, APIs, streaming platforms, and external sources to support a broad range of analytical use cases.<br>• Mentor team members and contribute ideas that streamline processes, reduce friction, and strengthen engineering best practices.