<p>We are looking for a Principal Data Engineer to shape and lead a modern enterprise data platform serving multiple business lines. This position will define the architectural direction for a Microsoft Fabric-based environment, guide engineering best practices, and partner closely with data and IT leaders to support long-term growth. The role is ideal for a senior technical expert who combines deep hands-on engineering capability with strong mentorship and platform strategy experience.</p><p><br></p><p>Responsibilities:</p><p>• Lead the design and ongoing evolution of a Microsoft Fabric lakehouse environment, establishing a scalable architecture across raw, refined, and curated data layers.</p><p>• Create and support robust data integration workflows that bring information together from more than 30 enterprise applications and operational platforms.</p><p>• Set technical standards for data engineering, including coding practices, solution design patterns, documentation, and review processes.</p><p>• Mentor data engineers through hands-on coaching, collaborative development, and detailed code feedback to strengthen team capability.</p><p>• Partner with data and IT leadership to align platform architecture with business priorities and future expansion needs.</p><p>• Develop high-quality data pipelines using modern tools and frameworks such as SQL, Python, Spark, and PySpark within cloud-based data ecosystems.</p><p>• Evaluate new technologies, architectural approaches, and data platform trends, then provide informed recommendations for adoption.</p><p>• Support governance and platform reliability by promoting strong lineage, quality, performance, and scalable engineering practices.</p><p>• Contribute to cross-platform interoperability initiatives, including environments that may involve Databricks, governance tooling, and enterprise data services.</p>
Position: Principal Data Engineer | Direct Hire Permanent<br>Location: Remote<br>Salary: $180,000 - $225,000 base plus SIGNIFICANT bonus and or equity potential<br><br>*** For immediate and confidential consideration, please send a message to MEREDITH CARLE on LinkedIn or send an email to me with your resume. My email can be found on my LinkedIn page. ***<br><br>Build the data foundation that powers analytics, AI, and enterprise decision-making.<br>We’re hiring a Principal Data Engineer to design and deliver a modern, scalable data platform from the ground up. This is a high-impact, hands-on role where you’ll lead architecture decisions, build production-grade pipelines, and enable secure, multi-tenant data capabilities that support everything from reporting to advanced AI use cases.<br><br>What You’ll Own<br> • Design and build a modern data platform across ingestion, transformation, storage, and consumption<br> • Develop scalable batch and real-time pipelines across diverse data sources (relational, event, document)<br> • Establish data architecture standards, modeling practices, and engineering frameworks<br> • Create secure, multi-tenant data environments with strong governance and access controls<br> • Build data models and serving layers for reporting, self-service analytics, and AI workloads<br> • Evaluate and implement cloud data technologies for performance, cost, and scalability<br> • Partner with engineering and business leaders to align data strategy with company priorities<br> • Guide vendors and external partners while enforcing internal data standards and quality<br><br>What You Bring<br> • 10+ years of experience in data engineering, including large-scale architecture ownership<br> • Strong programming skills in Python and experience with tools like Spark, Kafka, or similar<br> • Expertise building batch and streaming pipelines in cloud environments (AWS preferred)<br> • Deep knowledge of data modeling for both transactional and analytical systems<br> • Hands-on experience with Snowflake, Redshift, or modern data platforms<br> • Strong understanding of data governance, privacy, and secure data design<br> • Experience with ETL frameworks, testing, and CI/CD-driven data workflows<br> • Ability to clearly communicate technical strategy and tradeoffs to senior stakeholders<br><br>Why This Role<br> • Architect and build a next-generation data platform from foundational level<br> • Direct impact on analytics, product insights, and AI capabilities<br> • High ownership, visibility, and influence across engineering and leadership<br> • Opportunity to define standards, tooling, and long-term data strategy<br><br>If you’re a hands-on data engineer who enjoys building scalable platforms and shaping how data is leveraged across an organization, this is a high-impact opportunity to lead from the front.<br><br><br>*** For immediate and confidential consideration, please send a message to MEREDITH CARLE on LinkedIn or send an email to me with your resume. My email can be found on my LinkedIn page. Also, you may contact me by office: 515-303-4654 or mobile: 515-771-8142. Or one click apply on our Robert Half website. No third party inquiries please. Our client cannot provide sponsorship and cannot hire C2C. ***
Position: Principal Data Engineer | Direct Hire Permanent<br>Location: Remote<br>Salary: $225,000 - 300,000 base plus bonus potential<br>*** For immediate and confidential consideration, please send a message to MEREDITH CARLE on LinkedIn or send an email to me with your resume. My email can be found on my LinkedIn page. ***<br><br>Build the data foundation that powers analytics, AI, and enterprise decision-making.<br>We’re hiring a Principal Data Engineer to design and deliver a modern, scalable data platform from the ground up. This is a high-impact, hands-on role where you’ll lead architecture decisions, build production-grade pipelines, and enable secure, multi-tenant data capabilities that support everything from reporting to advanced AI use cases.<br><br>What You’ll Own<br> • Design and build a modern data platform across ingestion, transformation, storage, and consumption<br> • Develop scalable batch and real-time pipelines across diverse data sources (relational, event, document)<br> • Establish data architecture standards, modeling practices, and engineering frameworks<br> • Create secure, multi-tenant data environments with strong governance and access controls<br> • Build data models and serving layers for reporting, self-service analytics, and AI workloads<br> • Evaluate and implement cloud data technologies for performance, cost, and scalability<br> • Partner with engineering and business leaders to align data strategy with company priorities<br> • Guide vendors and external partners while enforcing internal data standards and quality<br><br>What You Bring<br> • 10+ years of experience in data engineering, including large-scale architecture ownership<br> • Strong programming skills in Python and experience with tools like Spark, Kafka, or similar<br> • Expertise building batch and streaming pipelines in cloud environments (AWS preferred)<br> • Deep knowledge of data modeling for both transactional and analytical systems<br> • Hands-on experience with Snowflake, Redshift, or modern data platforms<br> • Strong understanding of data governance, privacy, and secure data design<br> • Experience with ETL frameworks, testing, and CI/CD-driven data workflows<br> • Ability to clearly communicate technical strategy and tradeoffs to senior stakeholders<br><br>Why This Role<br> • Architect and build a next-generation data platform from foundational level<br> • Direct impact on analytics, product insights, and AI capabilities<br> • High ownership, visibility, and influence across engineering and leadership<br> • Opportunity to define standards, tooling, and long-term data strategy<br><br>If you’re a hands-on data engineer who enjoys building scalable platforms and shaping how data is leveraged across an organization, this is a high-impact opportunity to lead from the front.
<p>Robert Half is working with a client in the Atlanta area seeking a Senior Lead Data Engineer.</p><ul><li>Lead the design, development, and optimization of enterprise data platforms and data warehouse solutions</li><li>Drive the migration and modernization of legacy data environments to Snowflake</li><li>Architect and build scalable ETL/ELT pipelines that support analytics, reporting, and business intelligence initiatives</li><li>Design and maintain high-performance data models, data architectures, and data integration frameworks</li><li>Implement data quality, governance, monitoring, and performance optimization best practices</li><li>Serve as the technical lead for data engineering initiatives and help define the long-term data strategy and roadmap</li><li>Partner closely with business stakeholders, product teams, and technical leadership to translate requirements into scalable solutions</li><li>Utilize Airflow and/or Astronomer to orchestrate and manage complex data workflows</li><li>Develop solutions leveraging Python, SQL, Snowflake, and cloud-based data technologies</li><li>Mentor and guide other engineers while influencing technical direction and engineering standards</li><li>Evaluate and implement modern data engineering tools, frameworks, and best practices</li><li>Support the growth and scalability of the organization's data ecosystem while ensuring reliable, trusted, and accessible data</li></ul>
<p><strong>Lead Data Engineer</strong></p><p><br></p><p><strong>Company Overview</strong> </p><p>A media organization is seeking a Lead Data Engineer to support enterprise data, analytics, and emerging technology initiatives. This role offers the opportunity to lead technical projects, mentor engineers, and help shape the organization's data strategy.</p><p><br></p><p><strong>Role Summary</strong> </p><p>The Lead Data Engineer will be a hands-on technical leader responsible for data engineering, analytics, automation, and AI-related initiatives. This role combines individual contribution, technical direction, team mentorship, and cross-functional collaboration.</p><p><br></p><p><strong>Key Responsibilities</strong></p><ul><li>Lead the design, development, and support of enterprise data solutions.</li><li>Provide technical leadership and establish development best practices.</li><li>Oversee data pipelines, integrations, reporting, and automation initiatives.</li><li>Partner with business and technology stakeholders to prioritize work and deliver solutions.</li><li>Monitor data quality, reliability, and operational performance.</li><li>Mentor team members and support their technical development.</li><li>Evaluate emerging technologies and identify opportunities for innovation.</li></ul>
<p><strong>Robert Half is working with a client who is looking to hire a Data Engineer</strong> to support a growing enterprise data and analytics initiative. This individual will help build and modernize the data pipelines, integrations, and cloud-based data infrastructure used across the organization.</p><p>The ideal candidate has experience working with large datasets, modern cloud data platforms, and production-grade ETL/ELT pipelines. This role will work closely with Data Architects, Analytics Engineers, BI teams, and business stakeholders as the organization expands its enterprise data capabilities.</p><p>Responsibilities</p><ul><li>Design, build, and maintain scalable ETL/ELT data pipelines.</li><li>Integrate structured and unstructured data from multiple enterprise systems.</li><li>Develop and optimize data models used for analytics and reporting.</li><li>Build solutions within cloud data environments such as Azure, AWS, or GCP.</li><li>Monitor data pipelines for performance, reliability, and data quality.</li><li>Partner with analysts, architects, and application teams to understand data requirements.</li><li>Support the migration of legacy data workloads to modern cloud platforms.</li><li>Develop reusable frameworks and automation for data processing.</li></ul><p><br></p>
We are looking for a Data Engineer to help build and maintain scalable data solutions that support critical business operations within the brokerage industry. This role is based in Chicago, Illinois, and focuses on designing reliable data pipelines, improving data accessibility, and enabling efficient processing across large datasets. The ideal candidate brings strong technical expertise in modern data engineering tools and enjoys working in a fast-paced environment where data quality, performance, and consistency are essential.<br><br>Responsibilities:<br>• Design, develop, and optimize data pipelines that support ingestion, transformation, and delivery of large-scale datasets.<br>• Build and maintain ETL workflows to ensure accurate, timely, and dependable movement of data across platforms.<br>• Use Python and Apache Spark to process complex data efficiently and improve overall pipeline performance.<br>• Work with Apache Hadoop technologies to manage distributed data storage and computation for high-volume workloads.<br>• Implement streaming and messaging solutions with Apache Kafka to support near-real-time data integration needs.<br>• Partner with cross-functional teams to understand data requirements and translate business needs into technical solutions.<br>• Monitor data processes, troubleshoot issues, and resolve bottlenecks to maintain system reliability and data integrity.<br>• Contribute to enhancements involving data platform changes or internal process updates as part of ongoing engineering initiatives.
<p>We are looking for a Data Engineer to design, build, and maintain scalable data pipelines on Microsoft Azure. This role will support enterprise reporting, analytics, and machine learning initiatives by ensuring the availability and integrity of critical business data.</p><p>Responsibilities</p><ul><li>Design and develop ETL/ELT data pipelines</li><li>Build and maintain Azure data platforms and data lakes</li><li>Integrate data from APIs, databases, and third-party systems</li><li>Develop data models supporting analytics and reporting</li><li>Optimize database and pipeline performance</li><li>Implement data quality and monitoring solutions</li><li>Collaborate with analysts, developers, and business users</li></ul><p><br></p>
<p>We are looking for a Data Engineer to join an immediate contract opportunity in Woodbury, Minnesota. In this role, you will help advance a customer-focused analytics platform by creating reliable data solutions that support reporting, APIs, and interactive insights. You will work closely with technical and business partners to shape scalable data structures and deliver high-quality information products in a collaborative environment.</p><p><br></p><p>Responsibilities:</p><p>• Create and support robust data pipelines that move and prepare information for analytics and downstream applications.</p><p>• Build transformation workflows using dbt and optimize processing logic with Python, Spark SQL, and PySpark.</p><p>• Coordinate scheduled and dependency-driven data jobs with Apache Airflow to ensure consistent delivery.</p><p>• Ingest and manage data within an on-premises environment, maintaining efficiency, quality, and availability.</p><p>• Develop data services and API-ready outputs so customers can access information through external BI and analytics tools.</p><p>• Contribute to semantic and visualization layers, including tools such as Cube.dev or similar platforms, to improve analytics usability.</p><p>• Partner with application and dashboard developers to align data structures with customer-facing reporting needs.</p><p>• Work with stakeholders across product, business, and engineering teams to define solutions that support platform goals and user expectations.</p><p>• Apply modern development practices and AI-assisted tools where appropriate to improve productivity and accelerate delivery.</p>
<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>
<p>Robert Half is seeking an <strong>Enterprise Data Modeler</strong> to support a major enterprise data initiative for a professional services organization based in Seattle, WA. This consultant will help develop a governed semantic data layer using Denodo, creating consistent business definitions and trusted data models to support reporting, analytics, and AI-assisted data access.</p><p><br></p><p><strong>Duration: </strong>6 months contract to hire </p><p><strong>Location: </strong>100% remote </p><p><strong>Schedule: </strong>Monday - Friday - CST hours </p><p><br></p><p><strong>Key Responsibilities</strong></p><ul><li>Design and maintain conceptual, logical, and semantic data models that establish consistent business definitions and relationships across enterprise data.</li><li>Build and support Denodo base, derived, interface, and semantic views for reporting, analytics, and AI applications.</li><li>Apply enterprise data modeling standards for naming conventions, keys, relationships, reusable entities, and certified reporting views.</li><li>Reconcile conflicting data definitions, identifiers, and business rules across multiple source systems.</li><li>Define and maintain Denodo metadata, associations, descriptions, and business terminology to support reporting tools and AI-assisted querying.</li><li>Collaborate with data governance and security teams to support data classification, lineage, privacy, and access controls.</li><li>Align data models with master data management (MDM) standards, business glossaries, and data catalog tools such as Collibra.</li><li>Support AI and MCP-based use cases by defining business terminology, synonyms, sample questions, and testing criteria for data query accuracy.</li><li>Maintain documentation, including data dictionaries, entity catalogs, source-to-target mappings, lineage, and modeling standards.</li></ul>
<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 Senior Data Engineer to help shape and expand a modern enterprise data ecosystem in Dallas, Texas. This role is ideal for a highly technical specialist who enjoys building scalable cloud-based data solutions, improving platform performance, and collaborating with both engineering and business leaders. The position offers the opportunity to contribute directly to lakehouse design, advanced pipeline development, and data initiatives that support analytics and emerging AI use cases.<br><br>Responsibilities:<br>• Architect and develop scalable data platforms using Azure Databricks, Spark, PySpark, Python, Delta Lake, and related big data technologies.<br>• Create and maintain layered lakehouse data models across raw, refined, and curated environments to support enterprise reporting and analytics.<br>• Lead the movement of legacy data assets into Azure-based cloud environments as part of broader platform modernization efforts.<br>• Build, enhance, and monitor high-volume ETL and streaming workflows, notebooks, and distributed processing jobs for reliability and efficiency.<br>• Integrate tools such as Azure Synapse Analytics and Azure Data Factory to support end-to-end data ingestion, transformation, and delivery.<br>• Improve performance of large-scale data workloads by tuning Spark jobs, optimizing code, and applying engineering best practices.<br>• Establish and follow modern software delivery standards through source control, Azure DevOps pipelines, CI/CD processes, and deployment automation.<br>• Prepare and structure data for machine learning, generative AI, and MLOps initiatives, including feature creation and production-ready integration.<br>• Work closely with implementation partners, architects, technical teams, business stakeholders, and senior leadership to align solutions with organizational goals.<br>• Contribute hands-on engineering expertise while also helping guide technical design and platform architecture decisions.
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 an experienced Data Engineer to join a construction and contractor-focused organization in Appleton, Wisconsin. This contract opportunity with potential for a permanent role is ideal for a senior-level candidate who enjoys building scalable cloud-based data platforms, working hands-on with Python and notebook-driven development, and applying AI-enabled tools to create practical business solutions. The role will focus on designing modern data lake capabilities, improving data movement and transformation processes, and partnering with stakeholders to deliver reliable analytics infrastructure.<br><br>Responsibilities:<br>• Design, build, and enhance modern data lake architecture in Google Cloud Platform to support scalable and efficient data operations.<br>• Develop robust data pipelines using Python, SQL, Spark, and ETL frameworks to ingest, transform, and prepare data from multiple sources.<br>• Create and maintain notebook-based solutions that demonstrate clear technical approaches, reusable logic, and well-documented project outcomes.<br>• Integrate large-scale data processing technologies such as Hadoop and Kafka to support high-volume and streaming data workloads.<br>• Collaborate with cross-functional teams to translate business needs into data engineering solutions that improve reporting, analytics, and operational decision-making.<br>• Apply AI-driven tools and approaches to accelerate development, improve solution quality, and deliver innovative customer-focused outcomes.<br>• Support cloud data environments that may include Azure Data Lake and related platforms as part of broader enterprise data initiatives.<br>• Contribute to data platform improvements, including work connected to enterprise tool adoption or internal platform changes when needed.
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.
We are looking for a Data Engineer to help shape and expand a cloud-focused data environment that supports analytics, operational reporting, automation, and emerging AI use cases. Based in Brookfield, Wisconsin, this position works across technical and business teams to deliver dependable data solutions that improve access, accuracy, and usability. The role is ideal for someone who thrives on translating complex data needs into scalable engineering outcomes and values collaboration, problem-solving, and continuous improvement.<br><br>Responsibilities:<br>• Design, build, and maintain scalable data pipelines that move and transform information for analytics, reporting, and operational needs.<br>• Partner with business stakeholders, analysts, software developers, and leaders to understand data requirements and turn them into reliable engineering solutions.<br>• Develop and refine data models and platform architecture to support performance, flexibility, and long-term growth.<br>• Implement ETL processes that integrate data from multiple sources while improving consistency, completeness, and accessibility.<br>• Use Python and distributed data technologies such as Apache Spark and Hadoop to process large and complex datasets efficiently.<br>• Support streaming and event-driven data workflows using tools such as Apache Kafka where real-time data delivery is needed.<br>• Monitor data quality, troubleshoot pipeline issues, and optimize workflows to ensure dependable delivery and strong system performance.<br>• Contribute to ongoing enhancements of the data platform by identifying opportunities to improve scalability, automation, and engineering standards.
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.
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 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.
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.
We are looking for a Data Engineer to support a short-term Contract engagement focused on assessing and strengthening OpenLink security role design for a recently acquired business. This position will play a key role in reviewing current configurations, identifying gaps in segregation of duties, and helping shape a more effective access model. The role is based in Cincinnati, Ohio, with flexibility for remote or hybrid work depending on experience and project needs.<br><br>Responsibilities:<br>• Evaluate the existing OpenLink security framework and determine how well current role assignments support appropriate access controls.<br>• Recommend and help design an improved security role structure that aligns with segregation-of-duties expectations across the environment.<br>• Configure and refine OpenLink settings to support a secure, scalable, and well-governed access model.<br>• Partner with project stakeholders to document findings, explain risk areas, and outline practical remediation options.<br>• Support analysis related to the acquired company’s OpenLink setup and identify where adjustments are needed for consistency and control.<br>• Contribute technical expertise during project discussions, providing guidance on role configuration best practices and implementation considerations.<br>• Use data engineering tools and scripting capabilities to assist with analysis, validation, and supporting technical tasks where applicable.
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.
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.
<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>