We are looking for a Data Engineer to join a team in Santa Monica, California on a Long-term Contract assignment. This position focuses on building dependable data solutions that connect multiple sources, support reporting needs, and improve access to trusted business information. The role offers a blend of pipeline development, dashboard ownership, data quality work, and cross-functional collaboration with partner teams.<br><br>Responsibilities:<br>• Design, validate, and release data pipelines that bring together information from varied source systems within an existing ingestion environment.<br>• Create and support connections to external platforms, APIs, and vendor tools so data remains current, consistent, and available for analysis.<br>• Oversee daily pipeline health, investigate processing issues, and partner with senior engineers to address failures and improve reliability.<br>• Develop and maintain roughly 10 business intelligence dashboards used by 2 to 3 stakeholder groups, keeping reporting useful and aligned with team needs.<br>• Improve dashboard quality by reviewing accuracy, optimizing performance, enhancing usability, and retiring reports that no longer provide value.<br>• Write and maintain data validation checks, including tests at the source level to strengthen data quality and reduce downstream issues.<br>• Support data modeling and transformation efforts using dbt to prepare clean, well-structured datasets for analytics and reporting.<br>• Handle ad hoc data requests submitted through an intake process, providing timely answers to business questions with clear supporting analysis.<br>• Produce and update documentation for pipelines, reporting assets, and operational workflows to promote maintainability and knowledge sharing.<br>• Take part in peer code review by submitting thoughtful feedback and incorporating suggestions to maintain strong engineering standards.
We are looking for an experienced Data Engineer for a permanent opportunity in Northern, Virginia. In this role, you will design reliable data solutions that support analytics and business decision-making while helping improve the overall data environment. The ideal candidate brings strong technical depth in modern data engineering practices and enjoys building scalable systems from the ground up. <br> Responsibilities: • Design, develop, and maintain robust data pipelines that collect, transform, and deliver high-quality data for reporting and analysis. • Create and optimize data models that support business intelligence, analytics, and operational use cases across the organization. • Build and manage ETL workflows using Python and related technologies to ensure efficient movement of data between systems. • Work with large-scale data processing tools such as Apache Spark and Hadoop to handle complex and high-volume datasets. • Integrate streaming and batch data sources using technologies such as Apache Kafka to support timely and reliable data availability. • Collaborate with analysts, engineers, and business stakeholders to translate data needs into scalable engineering solutions. • Improve data warehouse performance, structure, and reliability to support accurate and accessible enterprise data. • Use dbt and other modern data transformation practices to organize, test, and document datasets effectively.
We are looking for a Data Engineer to create and enhance scalable data solutions that strengthen business intelligence capabilities across the organization. This role partners with technology leaders, software teams, delivery stakeholders, and external partners to turn business needs into reliable technical outcomes. The position is well suited to someone who thrives in an agile setting, works confidently with minimal supervision, and collaborates effectively across teams. This opportunity is based in Nashville, Tennessee.<br><br>Responsibilities:<br>• Build and refine scalable data pipelines and platform components that support reporting, analytics, and enterprise intelligence initiatives.<br>• Assess modern cloud and big data technologies, gain working knowledge of new tools quickly, and recommend practical options based on business needs.<br>• Translate technical and business requirements into clearly defined tasks, implementation plans, and measurable deliverables.<br>• Write maintainable, efficient, and high-quality code that supports stable and dependable data solutions.<br>• Review existing processes and code for gaps or performance issues, then propose and implement improvements.<br>• Adjust priorities effectively in response to evolving project demands, timelines, and stakeholder expectations.<br>• Take ownership of delivered solutions by documenting design decisions and explaining functionality to team members and partners.<br>• Contribute to additional data engineering efforts that advance organizational goals and strengthen outcomes for end users.
<p>We are looking for a Data Analyst to turn complex healthcare, financial, and operational data into meaningful insights that guide leadership decisions. This position supports organizational performance by connecting information from clinical and business systems, building practical reporting tools, and highlighting trends that matter. Based in Milwaukee, Wisconsin, the role works closely with finance, operations, and clinical stakeholders to improve visibility, planning, and overall effectiveness. <strong><em>Local candidates to the Greater Milwaukee area with Healthcare Epic experience is desired. </em></strong></p><p><br></p><p>Responsibilities:</p><p>• Create and maintain dashboards, scorecards, and recurring reports that combine Epic data with financial and operational information from connected platforms.</p><p>• Work with leaders across finance, operations, and clinical teams to define measurable indicators and track performance in patient services, revenue activity, and resource use.</p><p>• Gather, clean, and evaluate large datasets to support forecasting, monthly business reviews, and long-range planning initiatives.</p><p>• Present findings in a clear, business-focused format so non-technical stakeholders can understand trends, risks, and opportunities.</p><p>• Collaborate with IT and data governance partners to uphold data accuracy, reporting consistency, and compliance with organizational standards.</p><p>• Identify patterns in clinical and administrative workflows and recommend improvements that increase efficiency and support better decision-making.</p><p>• Contribute to reporting automation and process enhancements that improve speed, reliability, and access to timely performance data.</p>
We are looking for a Data Engineer to join a Financial Services team in Plano, Texas on a contract basis with the potential for a permanent role. This role is focused on designing and delivering reliable data pipelines in a cloud-first environment, with Snowflake serving as a central platform for analytics and data consumption. The position offers a balanced mix of new development and targeted optimization, with an emphasis on improving data quality, operational visibility, and scalable processing capabilities.<br><br>Responsibilities:<br>• Design, build, and deploy end-to-end data pipelines with Snowflake as a primary data platform.<br>• Create new ingestion and transformation workflows while resolving issues affecting existing pipeline performance and reliability.<br>• Support streaming data integration using Apache Kafka to enable timely and scalable data movement.<br>• Strengthen observability across data workflows by improving monitoring, alerting, and pipeline transparency.<br>• Enhance data quality practices through validation, testing, and proactive issue identification.<br>• Modernize data architecture by reducing dependency on legacy processes and addressing technical debt.<br>• Contribute to engineering standards by applying disciplined development practices, code quality measures, and repeatable delivery methods.<br>• Help expand CI/CD and testing capabilities by promoting more consistent automation across build and release activities.<br>• Use AI-assisted development tools to accelerate coding, testing, and documentation where appropriate.
We are looking for a Data Engineer to join a Contract engagement supporting people analytics and HR-focused data initiatives in Philadelphia, Pennsylvania. This role is centered on designing, building, and refining scalable data pipelines and curated data assets using Azure Databricks and Azure Data Factory. The ideal candidate will be comfortable working hands-on with complex source data, translating business needs into reliable transformations, and delivering production-ready solutions with minimal oversight.<br><br>Responsibilities:<br>• Design, build, and maintain robust ETL and ELT workflows that support HR and workforce data products.<br>• Develop scalable data transformations using Python, SQL, Spark, and Azure Databricks to prepare high-quality datasets for downstream use.<br>• Orchestrate and monitor pipeline execution through Azure Data Factory to ensure dependable and timely data delivery.<br>• Analyze existing source systems and data structures to map requirements and create efficient ingestion and transformation logic.<br>• Partner with stakeholders across HR and analytics teams to turn business questions into practical engineering solutions.<br>• Improve data quality, performance, and reliability by applying modern engineering standards, testing practices, and documentation.<br>• Support integrations across HRIS and related platforms, including contributing to interface development where needed.<br>• Create production-ready datasets that enable reporting, workforce analysis, and broader people data initiatives.
<p>We are looking for a Data Platform Engineer to join a 4-month contract opportunity based in Norman, Oklahoma. This role owns the storage, retention and pipeline-correctness workstream. This is data engineering, not cloud </p><p>infrastructure.</p><p><br></p><p>What they will actually do</p><p>▪ Own the storage and pipeline backlog across the ingestion and processing repositories</p><p>▪ Event ordering and bitemporal modelling: separating event time from ingestion time, deterministic replay</p><p>▪ Atomic snapshot publication with schema admission and rollback</p><p>▪ Design the historical layer on object storage so it can be queried economically and answer as-of </p><p>questions correctly — file format, partitioning, compaction, and whether the canonical layer moves </p><p>onto a managed table format</p><p>▪ Reconcile the operational database and the historical store so a point-in-time question returns one </p><p>answer rather than two</p><p>▪ Evidence immutability and retention: object-lock semantics, preventing overwrite of stored evidence, </p><p>validated recovery paths</p><p>▪ Pipeline durability: idempotent runs, completeness receipts, restore testing</p><p>▪ Support production cutover in month 4 — this seat does not roll off early</p>
We are looking for a Data Engineer to join our Data & Analytics team in Houston, Texas, and help strengthen the reporting foundation that supports decisions across the business. In this role, you will shape data into reliable, business-ready models, develop scalable reporting structures, and work closely with teams such as Finance and Operations to turn complex rules into clear insights. This position blends technical data engineering with analytics-focused development, offering the opportunity to improve reporting quality, maintain dependable data flows, and contribute to ongoing enhancements in our analytics environment.<br><br>Responsibilities:<br>• Develop and maintain structured data models, including staging layers, reporting tables, and dimensional designs within the Azure-based data warehouse environment.<br>• Create and support Power BI semantic models, calculated measures, security configurations, dashboards, and reports used for operational and enterprise reporting.<br>• Monitor scheduled data workflows and dataset refresh activity, investigate processing issues, and resolve failures to keep reporting available and accurate.<br>• Adjust and enhance existing ETL and orchestration processes as business needs evolve, using tools such as Azure Data Factory and related Microsoft data platforms.<br>• Collaborate with stakeholders across functions to translate reporting needs, financial logic, and operational rules into dependable analytical solutions.<br>• Produce clear technical and business documentation covering model design, metric definitions, transformation logic, and data ownership.<br>• Apply and help refine development standards for naming, modeling approach, query design, and reporting structure to improve consistency and quality.<br>• Identify opportunities to streamline current data practices, improve performance, and introduce effective tools or methods that strengthen analytics capabilities.
<p>A Manufacturing/ distribution company is looking for a Data Engineer with 3 + years of experience to join a dynamic team in Oklahoma City, Oklahoma. In this role, you will play a crucial part in designing and maintaining the data infrastructure to support analytics and decision-making processes. You will be a key contributor in developing, optimizing, and maintaining the data infrastructure that supports analytics and business intelligence initiatives, and data driven decision-making using Snowflake, Matillion, and other tools. Position will be in-office to work closely with the team. You must live in the Oklahoma City area. Client is unable to sponsor. No 3rd parties please.</p><p><br></p><p>Responsibilities:</p><p><br></p><p>• Design, develop, and maintain scalable data pipelines to support data integration and real-time processing.</p><p>• Implement and manage data warehouse solutions, with a strong focus on Snowflake architecture and optimization.</p><p>• Write efficient and effective scripts and tools using Python to automate workflows and enhance data processing capabilities.</p><p>• Work with SQL Server to design, query, and optimize relational databases in support of analytics and reporting needs.</p><p>• Monitor and troubleshoot data pipelines, resolving any performance or reliability issues.</p><p>• Ensure data quality, governance, and integrity by implementing and enforcing best practices.</p>
Position: Data Engineer<br>Location: Oskaloosa, IA / Des Moines, IA -- Onsite<br>Salary: $100,000 - $120,000 base + exceptional benefits<br><br>*** For immediate and confidential consideration, please APPLY and EMAIL YOUR RESUME to MEREDITH CARLE . My email can be found on my LinkedIn page. ***<br><br>Data Engineer<br>Shape the Future of Enterprise Analytics, AI & Data Innovation<br>Are you the type of data professional who sees a business problem and immediately starts thinking about how to model, engineer, and deliver the right solution?<br>A highly successful, technology-driven organization is seeking a Data Engineer to help build the foundation for enterprise analytics, business intelligence, governance, and next-generation AI initiatives. This is an opportunity to make a visible impact across the business while working alongside a collaborative team of data professionals who are passionate about solving complex challenges.<br>This role is ideal for someone who enjoys being hands-on, partnering with the business, and building modern data solutions that drive meaningful outcomes.<br>What You'll Do<br> • Design and develop scalable data models, semantic layers, and enterprise data solutions<br> • Build trusted data structures that support analytics, reporting, AI, and business intelligence initiatives<br> • Develop and optimize cloud-based data pipelines and integrations<br> • Improve data governance, lineage, metadata management, and observability practices<br> • Create automated data quality and validation frameworks<br> • Implement CI/CD processes and modern DataOps best practices<br> • Optimize SQL performance across warehouses, lakehouses, and reporting environments<br> • Partner with analysts and business stakeholders to transform complex problems into actionable solutions<br> • Contribute to technical standards, mentoring, documentation, and architectural best practices<br>What We're Looking For<br>Core Qualifications<br> • Strong experience with data modeling and semantic modeling<br> • Hands-on experience with Microsoft Fabric, Azure, and Power BI<br> • Advanced SQL development and performance optimization skills<br> • Experience with Git, CI/CD, and modern data engineering practices<br> • Ability to translate business requirements into scalable technical solutions<br> • Experience developing enterprise data pipelines and analytics platforms<br> • A plus if you have data governance, data warehouse, data lake and or DBT experience.<br><br>*** For immediate and confidential consideration, please APPLY and EMAIL YOUR RESUME to MEREDITH CARLE . My email can be found on my LinkedIn page. Also, you may contact me at 515-303-4654. Or one click apply on our Robert Half website. No third party inquiries please. Our client cannot provide sponsorship and cannot hire C2C. ***
We are looking for an experienced Data Engineer to support the design and delivery of scalable data solutions within the People and HR data domain in Philadelphia, Pennsylvania. This Long-term Contract position is ideal for someone who can step into an existing environment, learn source systems quickly, and build reliable data pipelines that support reporting and business analysis. The role centers on hands-on development using Azure Databricks, Spark, and Azure Data Factory, with an emphasis on creating production-ready data assets. Experience working with HR data and workforce-related systems will be highly valuable in helping translate business needs into dependable engineering solutions.<br><br>Responsibilities:<br>• Design, build, and maintain data pipelines that ingest, transform, and prepare HR and workforce data for downstream analytics and reporting.<br>• Develop scalable data processing solutions using Azure Databricks, Python, Spark, and SQL to support production-grade data products.<br>• Orchestrate and monitor data workflows through Azure Data Factory to ensure timely, accurate, and resilient data movement.<br>• Analyze existing data sources and business requirements to create efficient transformation logic and structured datasets.<br>• Collaborate with stakeholders across technical and functional teams to align data engineering deliverables with HR reporting and operational needs.<br>• Improve data quality, performance, and reliability by applying sound ETL practices, validation checks, and optimization techniques.<br>• Support integration efforts involving HR systems and related interfaces, including work connected to evolving source platforms when needed.<br>• Contribute to documentation, technical standards, and knowledge sharing to support maintainability and long-term solution success.
<p>Robert Half is seeking an experienced <strong>Senior Data Engineer</strong> to support a <strong>law firm based in Seattle, Washington</strong> with the continued development and modernization of its enterprise data environment. This role will focus heavily on <strong>Databricks, dimensional modeling, PySpark, and Unity Catalog</strong>, with responsibility for ingesting, transforming, and preparing data from multiple enterprise systems for reporting and analytics.</p><p> </p><p><strong>Duration:</strong> 6-month contract</p><p><strong>Location:</strong> 100% Remote – preferred to work PST or CST</p><p><strong>Pay rate: </strong>$65-$75/hour (W2)</p><p> </p><p><strong>Responsibilities</strong></p><ul><li>Design, develop, and maintain scalable data pipelines and data models within <strong>Databricks</strong>.</li><li>Build and optimize <strong>dimensional data models</strong> to support enterprise reporting and analytics.</li><li>Develop data transformation and processing workflows using <strong>PySpark</strong>.</li><li>Ingest data from enterprise source systems, including <strong>Workday, Elite/Elite 3E, and Salesforce</strong>.</li><li>Move data through the <strong>Medallion Architecture</strong>, including Bronze, Silver, and Gold layers.</li><li>Clean, normalize, transform, and prepare source data for downstream analytics and reporting.</li><li>Utilize <strong>Unity Catalog</strong> to support data governance, security, organization, and access management.</li><li>Prepare curated datasets for consumption and reporting within <strong>Power BI</strong>.</li><li>Partner with business and technical stakeholders to understand reporting needs and demonstrate data within Power BI.</li><li>Troubleshoot data connectivity and integration issues involving <strong>APIs, Postman, firewall rules, and certificates</strong>.</li><li>Ensure data pipelines and models are scalable, reliable, and aligned with data engineering best practices.</li></ul><p><br></p>
We are looking for an experienced Senior Data Engineer to help design and enhance scalable data platforms that support critical business operations in Houston, Texas. This role is ideal for someone who enjoys building reliable pipelines, improving data accessibility, and working with modern big data technologies in a service-focused environment. The successful candidate will play a key role in shaping data architecture, enabling analytics, and maintaining high standards for performance, quality, and governance.<br><br>Responsibilities:<br>• Design, build, and optimize large-scale data pipelines that collect, transform, and deliver data from multiple sources.<br>• Develop robust ETL workflows using Python and Spark to support reporting, analytics, and operational data needs.<br>• Implement and maintain data solutions across distributed processing environments, including Hadoop and Azure Databricks.<br>• Create streaming and batch data integrations using Kafka and related technologies to ensure timely and dependable data movement.<br>• Collaborate with analysts, engineers, and business stakeholders to translate requirements into scalable technical solutions.<br>• Monitor pipeline performance, troubleshoot data issues, and apply improvements that increase reliability and efficiency.<br>• Establish data quality controls, validation processes, and documentation to support governance and long-term maintainability.<br>• Contribute to platform enhancements and technical initiatives, including changes to data infrastructure or internal processing frameworks when needed.
We are looking for an experienced Lead Data Engineer to oversee the design, implementation, and management of advanced data infrastructure in Houston, Texas. This role requires expertise in architecting scalable solutions, optimizing data pipelines, and ensuring data quality to support analytics, machine learning, and real-time processing. The ideal candidate will have a deep understanding of Lakehouse architecture and Medallion design principles to deliver robust and governed data solutions.<br><br>Responsibilities:<br>• Develop and implement scalable data pipelines to ingest, process, and store large datasets using tools such as Apache Spark, Hadoop, and Kafka.<br>• Utilize cloud platforms like AWS or Azure to manage data storage and processing, leveraging services such as S3, Lambda, and Azure Data Lake.<br>• Design and operationalize data architecture following Medallion patterns to ensure data usability and quality across Bronze, Silver, and Gold layers.<br>• Build and optimize data models and storage solutions, including Databricks Lakehouses, to support analytical and operational needs.<br>• Automate data workflows using tools like Apache Airflow and Fivetran to streamline integration and improve efficiency.<br>• Lead initiatives to establish best practices in data management, facilitating knowledge sharing and collaboration across technical and business teams.<br>• Collaborate with data scientists to provide infrastructure and tools for complex analytical models, using programming languages like Python or R.<br>• Implement and enforce data governance policies, including encryption, masking, and access controls, within cloud environments.<br>• Monitor and troubleshoot data pipelines for performance issues, applying tuning techniques to enhance throughput and reliability.<br>• Stay updated with emerging technologies in data engineering and advocate for improvements to the organization's data systems.
A leading infrastructure and transportation group is seeking a hands-on Senior Data Engineer to architect and build our next-generation Enterprise Reporting & Data Warehouse (ERDW.Next) on Databricks. Acting as the hands-on technical owner, you will drive target-state design, medallion architecture (Delta Lake), and legacy migrations off SQL Server/Synapse while remaining actively in the codebase. You will partner with our Enterprise Architect, Power BI team, business users, and offshore delivery partners across finance, construction, and safety domains. <br> experience in CANDIDATES SHOULD HAVE EXPERIENCE WITH: Serving as a Databricks SME ERP integrations (strong plus) Python / PySpark Performance tuning and optimization Databricks maintenance, including VACUUM Data governance and best practices Semantic modeling <br> RESPONSIBILITIES Architecture & Strategy: Design the ERDW.Next lakehouse (medallion/gold layer star schema) and govern data modeling, batch/streaming, and Delta Live Tables standards. Build & Migration: Write production-grade PySpark, Spark SQL, and Delta Live Tables pipelines orchestrated via Databricks Workflows. Re-engineer legacy SSIS/T-SQL/Synapse logic and ingest ERP data (JD Edwards, Anaplan) using CDC and Auto Loader. Platform Operations: Optimize cost/performance using Photon, Liquid Clustering, and OPTIMIZE. Manage CI/CD pipelines via Git and Databricks Asset Bundles while maintaining strict SLAs. Governance & AI: Implement Unity Catalog governance (entitlements, lineage, audit) and curate Databricks Genie Agents for natural-language analytics. Partner & Legacy Management: Support legacy platforms during transition and maintain code quality standards across offshore partner teams. Leverage agentic AI coding tools safely to accelerate delivery.
We are looking for a skilled Database Engineer to join our team in Westlake, Ohio. In this role, you will design, implement, and optimize database solutions to support business operations and data-driven decision-making. You will collaborate with cross-functional teams and leverage advanced technologies to ensure robust database performance and scalability.<br><br>Responsibilities:<br>• Design and implement database solutions that align with business requirements and technical specifications.<br>• Optimize and tune database performance to ensure efficiency and scalability.<br>• Collaborate with cross-functional teams to analyze data needs and develop appropriate solutions.<br>• Write, test, and troubleshoot complex SQL queries, including joins, aggregations, and stored procedures.<br>• Monitor and maintain database systems, performing regular updates and performance checks.<br>• Utilize source control and CI/CD tools, such as Azure DevOps, to manage database development and deployment.<br>• Stay updated on emerging technologies and integrate new tools into existing systems as needed.<br>• Provide technical guidance and support to team members regarding database-related issues.<br>• Ensure data integrity and security through regular audits and implementation of best practices.
We are looking for a Data Scientist to help strengthen turbine reliability and improve operational visibility across a power generation fleet in Houston, Texas. This position combines hands-on data preparation, advanced analytics, and business reporting to turn complex equipment and performance data into practical insights for engineering and leadership teams. The ideal candidate can move comfortably between developing dashboards, analyzing downtime patterns, and creating predictive tools that support maintenance and performance decisions.<br><br>Responsibilities:<br>• Create and enhance dashboard reporting that highlights fleet financial results, equipment availability, and the primary factors affecting cost and performance.<br>• Convert operational and maintenance data into meaningful metrics and recurring reports for engineering teams, business partners, and leadership stakeholders.<br>• Gather, organize, and refine downtime, outage, and maintenance records from multiple operational sources and field documentation to support reliable analysis.<br>• Evaluate failure and outage events to identify root causes and categorize trends across mechanical, electrical, controls, weather, and grid-related issues.<br>• Build and maintain a dependable historical data set that supports querying, trend analysis, and long-term reliability reviews across the turbine fleet.<br>• Design and implement statistical and machine learning models that anticipate downtime risk, detect component deterioration, and support earlier intervention.<br>• Develop anomaly detection and early alert solutions using sensor and operating data such as vibration, temperature, pressure, and combustion-related measurements.<br>• Partner with reliability, operations, engineering, and asset management teams to validate analytical findings, improve data pipelines, and recommend maintenance strategy adjustments based on observed trends.<br>• Present technical conclusions and business implications clearly to both specialized and non-technical audiences, including management and leadership teams.
<p>100% Onsite Role. We are looking for a Data Scientist to support advanced analytics and machine learning initiatives in Sacramento, California. This Long-term Contract position focuses on creating practical, production-ready solutions that improve operational decision-making, with an early emphasis on predictive maintenance and fleet performance in asset-heavy environments. The role works closely with business leaders and technical teams to turn complex data into reliable models, useful insights, and scalable AI capabilities. Must have experience in construction, mining, transportation, manufacturing, energy, or other asset-intensive industries</p><p><br></p><p>Responsibilities:</p><p>• Build, test, and implement machine learning and analytical models using operational, maintenance, and telemetry data to improve equipment reliability and reduce unexpected downtime.</p><p>• Create pilot solutions with measurable success criteria, then develop high-performing concepts into stable model pipelines suitable for ongoing production use.</p><p>• Convert analytical results into clear recommendations, dashboards, or decision-support outputs that help both technical teams and business stakeholders act with confidence.</p><p>• Partner with data engineering teams to shape requirements for data intake, transformation, feature creation, and model delivery across the broader data environment.</p><p>• Document methodologies, assumptions, dependencies, and performance results to support transparency, repeatability, and effective model governance.</p><p>• Provide input on enterprise data architecture needs from a data science perspective, including dataset design, feature availability, experiment tracking, and model readiness.</p><p>• Identify data limitations, quality concerns, and enrichment opportunities that could influence model accuracy or business value.</p><p>• Work with cross-functional partners to define new analytics and AI opportunities and assess external tools or vendor-developed solutions when needed.</p>
We are looking for a Data Scientist to join a healthcare-focused technology team in Bala Cynwyd, Pennsylvania on a Long-term Contract basis. This position will support predictive analytics initiatives tied to state programs, quality oversight, compliance monitoring, and risk evaluation. The role is well suited for someone who can work comfortably across technical analysis, stakeholder communication, and team guidance while using modern Microsoft-based data tools.<br><br>Responsibilities:<br>• Build and refine predictive models that support healthcare analytics, quality measurement, compliance assessment, and risk-related reporting.<br>• Analyze complex datasets to uncover trends, generate actionable insights, and improve decision-making for program and operational stakeholders.<br>• Create interactive dashboards and reporting solutions in Power BI to present findings in a clear and business-friendly format.<br>• Use R, Python, SQL, and related tools to prepare data, perform statistical analysis, and develop repeatable analytical workflows.<br>• Work within Azure and Microsoft-based data environments, including SQL Server and MySQL, to manage and access structured data sources.<br>• Partner with analysts and business teams to translate technical results into practical recommendations for non-technical audiences.<br>• Provide day-to-day guidance and informal leadership within a small analytics team, helping coordinate priorities and support best practices.<br>• Contribute to data engineering and integration activities, including ETL processes and the use of tools such as Spark, Hadoop, and Kafka where needed.<br>• Leverage approved AI tools such as Copilot to improve efficiency in analysis, documentation, and reporting tasks.
<p>As our portfolio of AI-driven solutions continues to expand, we’re looking for an experienced Machine Learning Engineer to join our high-impact data science team. This role offers the opportunity to work across trading, operations, and support functions—delivering production-grade machine learning systems that solve real business problems.</p><p>You’ll collaborate with data scientists, software engineers, and commercial stakeholders to design, build, and deploy models that drive decision-making and innovation. From project scoping to model deployment, you’ll have visibility and influence across the full ML lifecycle.</p><p>🔧 Core Responsibilities</p><ul><li>Act as a thought partner to commercial teams, identifying high-value opportunities for AI/ML applications</li><li>Lead the design, development, and deployment of machine learning systems, with a focus on NLP, LLMs, and Generative AI</li><li>Prioritize projects based on business impact and evolving market conditions</li><li>Collaborate with cross-functional teams to gather requirements and align solutions with strategic goals</li><li>Integrate ML solutions—including GenAI—into existing platforms to ensure seamless user experiences and scalable adoption</li><li>Participate in code reviews, experiment design, and tooling decisions to maintain high engineering standards</li><li>Share knowledge and mentor colleagues to build machine learning fluency across the organization</li></ul>
We are looking for a Snowflake Data Engineer to build modern data solutions that support analytics, AI, and enterprise decision-making. This role will focus on creating reliable pipelines, scalable data models, and secure data products within a Snowflake environment. The position is based in Radnor, Pennsylvania, and offers the opportunity to work closely with engineering, security, product, and business teams on high-impact initiatives.<br><br>Responsibilities:<br>• Architect and maintain scalable data pipelines, transformation workflows, and structured data models within Snowflake to support reporting, analytics, and AI use cases.<br>• Develop and deploy AI and machine learning capabilities using Snowflake-native tools, including intelligent agents, search-driven experiences, and customer-facing applications where applicable.<br>• Define and manage semantic views and business logic layers that provide governed, consistent definitions for analytics and AI consumption.<br>• Implement and support data ingestion frameworks using technologies such as Kafka, Snowpipe, APIs, Openflow, and third-party connectors, including integrations with enterprise platforms like NetSuite.<br>• Create reusable engineering utilities and automation with SQL, SnowSQL or related Snowflake tooling, Python, and JavaScript when appropriate.<br>• Establish strong production standards through automated testing, data quality validation, observability, exception handling, and controlled deployment processes.<br>• Tune queries, pipelines, AI workloads, and data structures to improve performance, scalability, maintainability, and downstream usability.<br>• Design secure data-sharing solutions and data-product delivery patterns for internal stakeholders, external partners, and customers.<br>• Collaborate with platform administrators and security teams to align access controls, governance requirements, identity integration, and production readiness for Snowflake solutions.<br>• Investigate and resolve issues affecting pipelines, semantic models, applications, and data quality while partnering across technical and business teams to deliver durable solutions.
<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>Robert Half is working with a client who is looking to hire a Machine Learning Engineer</strong> to help operationalize machine learning models as part of a growing enterprise AI program.</p><p>This role will sit between Data Science and Engineering and will focus on taking models from experimentation into reliable, scalable production environments.</p><p>Responsibilities</p><ul><li>Develop and deploy machine learning models into production.</li><li>Build reusable ML pipelines for training, testing, deployment, and monitoring.</li><li>Partner with Data Scientists to productionize predictive models.</li><li>Develop APIs and services that expose machine learning capabilities to enterprise applications.</li><li>Monitor model performance, drift, and reliability.</li><li>Build automated testing and deployment processes for ML workloads.</li><li>Optimize model performance and infrastructure utilization.</li><li>Work with Data Engineering teams to establish reliable training and inference datasets.</li></ul><p><br></p>
<p>Join a growing data engineering team supporting mission-critical analytics and business operations within the <strong>energy, commodities trading, oil & gas, or financial services sector</strong>. This role is focused on designing and supporting cloud-based data solutions using <strong>Python, Snowflake, and AWS</strong>, enabling scalable, reliable, and high-performance data platforms.</p><p>You will work closely with business stakeholders, analysts, and engineering teams to build production-grade data pipelines, modernize legacy data environments, and deliver trusted datasets that support operational and strategic decision-making.</p><p><br></p><p>Responsibilities</p><ul><li>Design, develop, and maintain scalable <strong>Python-based data pipelines</strong> that ingest, transform, and deliver data from internal and external sources.</li><li>Build and optimize data models within <strong>Snowflake</strong>, ensuring performance, scalability, and cost efficiency.</li><li>Develop ELT/ETL solutions leveraging cloud-native AWS services.</li><li>Create, monitor, and support batch and near real-time data integration workflows.</li><li>Work with structured, semi-structured, and time-series datasets from operational, trading, financial, and market data systems.</li><li>Modernize legacy data processes and migrate legacy database workloads into modern cloud architectures.</li><li>Implement data quality, reconciliation, monitoring, and alerting capabilities across the data platform.</li><li>Collaborate with analysts, traders, business users, and technology teams to deliver data solutions aligned with business objectives.</li><li>Participate in code reviews, testing, documentation, and deployment activities following engineering best practices.</li><li>Support production environments and troubleshoot data pipeline issues as needed.</li></ul><p><br></p><p><br></p><p><br></p>
The Data Engineer II will play a vital role in our Data transformation journey — helping move the organization toward a future state where decision-makers have the right data, in real time, when they need it. This role is responsible for driving the execution of key data projects, including data management, data conversions, ongoing ingestion pipelines, and data platform engineering and operations. The ideal candidate will work across systems and teams to ensure data flows seamlessly, reliably, and securely throughout the enterprise, supporting both strategic initiatives and day-to-day operations. <br> Duties and Responsibilities: <br> Develop an understanding of our current data landscape Administer and optimize Snowflake and other data platforms to ensure performance and alignment with business goals including role/permission management performance tuning, cost optimization, and capacity planning. Manage operations of the Data Warehouse, Middleware/Azure Enterprise Service Bus (ESB), Cognos, and related analytics tools. Provide secondary support for Data Integration & Platform Engineering responsibilities, including assisting with system maintenance, data pipeline operations, and platform enhancements. Stay informed on emerging data technologies and trends, sharing insights to help identify opportunities for innovation and improved data use. Help implement scalable and efficient data solutions that improve business insights and operational performance. Collaborate with IT and business stakeholders to support data access, usability, and consistency across departments. Participate in the integration of new tools and systems, ensuring seamless functionality and data accuracy. Provide advanced support by resolving escalated issues from Data Engineer I, ensuring timely and effective resolution of complex technical challenges. Contribute to a collaborative, high-performing team culture by supporting peers and sharing knowledge. Enforce data governance policies, ensuring data is secure, consistent, and compliant with regulations. Support efforts to meet financial regulations and cybersecurity standards in all data-related systems. Administrator and/or deliver configuration requests in our Azure ESB environment to Banking Core System through APIs. Design and develop pipelines for additional data vendors Recommend, implement and maintain best practices for the environment Ability to illustrate and maintain the enterprise’s data systems architecture Complies with all applicable banking laws and regulations, including, but not limited to the Bank Secrecy Act, must be eligible to work in the U.S. Patriot Act, and related anti-money laundering statutes, and federal consumer protection legislation and regulations. Builds working knowledge of all applicable laws and regulations. Other duties as assigned