Top Data Engineer Jobs

Designs, builds, optimizes, and supports scalable AWS-based data infrastructure, pipelines, data models, and ETL/ELT processes using Python, SQL, PySpark, Databricks, and Spark. The role supports analytics, reporting, and business intelligence through production monitoring, troubleshooting, testing, CI/CD, documentation, and cross-functional collaboration with BI, Product, Engineering, Data, and client-facing teams.

Develop and maintain Azure-based data platforms, architectures, pipelines, and processing solutions. Build scalable integrations with Azure Data Factory and Databricks, transform data for business intelligence, implement infrastructure as code and CI/CD, and perform testing, governance, documentation, and data quality activities. Collaborate with BI developers, business stakeholders, and source-system owners to translate requirements into secure, maintainable datasets and support future data warehousing, machine learning, streaming, and visualization needs.

Build and maintain Databricks data pipelines using Python, PySpark, Spark SQL, and Delta Lake. Ingest data from databases, APIs, files, and cloud storage; implement medallion architecture; optimize Spark workloads; add data quality checks; manage schema changes; and monitor workflows. The role also involves Git-based development, documentation, code reviews, CI/CD participation, and client-facing communication. It is hybrid and based at client sites in McLean, Virginia.

Build and maintain data engineering solutions using Python, PySpark, and big data technologies. Work with AWS services including S3, EMR, and Lambda, while supporting infrastructure through Terraform. Integrate Java, REST APIs, messaging systems, Kafka, Cassandra, Airflow, Control-M, and native schedulers. Databricks experience and familiarity with machine learning workflows are preferred.

Build and manage HR data pipelines, ETL/ELT processes, data warehouse models, and analytics workflows across cloud platforms. Ensure data quality, governance, security, and availability for People Analytics and HR stakeholders. Lead and mentor data engineers, collaborate with business and IT teams, and support reporting and analysis using tools such as Python, R, and business intelligence platforms.

Develop and maintain data lake and data mart architecture, translate client requirements into migration plans, extract and migrate data while preserving integrity and security, create data visualizations, and collaborate with QA to resolve defects. The role requires client-focused travel and/or relocation to unanticipated U.S. locations as project needs dictate.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.

Supports Microsoft SQL Server databases, T-SQL queries, integrations, scheduled jobs, and data integrity. Provides secondary support for Aderant Expert financial applications, including configuration, troubleshooting, testing, upgrades, and documentation. Assists with release preparation, deployment validation, application administration, and issue resolution while collaborating with IT, accounting, vendors, and project teams under senior guidance.

Build and operate production data pipelines connecting marketing, CRM, licensing, payment, and operational systems with Databricks. Develop PySpark and SQL ETL/ELT workflows, dimensional models, APIs, data quality monitoring, identity resolution, and customer segmentation data flows. Support HubSpot enrichment, lifecycle marketing, campaign attribution, funnel reporting, and marketing ROI analytics while maintaining documentation, alerting, and incident response processes.

Develop data engineering algorithms and systems that transform structured and unstructured data into actionable insights. Analyze data using statistical and general-purpose programming languages, apply scientific methods, and help clients understand and use complex data. Collaborate in fast-paced environments while supporting data gathering, cleansing, parsing, and analysis across Windows and Linux environments.

Architects, deploys, and operates large-scale Kafka and Confluent streaming platforms. Responsibilities include cluster sizing, security, automation, observability, HA/DR, infrastructure as code, troubleshooting, developer enablement, and partnering with application teams. The role requires deep Kafka expertise, production operations experience, strong scripting skills, and mentorship of junior engineers.

Develop data analytics and visual analytic applications using structured and semi-structured data. Design enterprise databases, build large relational databases, integrate systems, optimize data pipelines, and improve system performance. The role requires Java, AWS, ETL, Spark/Scala, SQL/PLSQL, PostgreSQL, NiFi, Informatica, Linux, an active Top Secret/SCI-eligible clearance, and potential CI polygraph eligibility.

Design, build, and maintain scalable batch and streaming data pipelines using Databricks, PySpark, Python, SQL, Spark, and Delta Lake. Develop transformations, orchestration workflows, data models, validation processes, and curated data products. Troubleshoot performance and quality issues, apply governance and security practices, document solutions, and collaborate with architects, data scientists, DevOps teams, analysts, and clients.

Develop and manage HR data pipelines, ETL processes, warehouse models, and data transformations for People Analytics. Integrate data from Workday, payroll, talent management, and other systems while ensuring quality, security, governance, and compliance. Mentor data engineers, support reporting workflows in Python and R, and collaborate with HR, IT, and business stakeholders to deliver reliable analytics solutions.

Designs and deploys scalable data pipelines and ETL/ELT processes using Azure and Databricks. Develops Python, PySpark, and SQL solutions, builds relational and analytical data models, optimizes pipeline performance, and ensures data quality. Serves as a technical SME for vendor developers through code reviews, quality standards, delivery oversight, and collaboration with product owners and business stakeholders.

Designs, develops, and maintains data models, integration pipelines, SQL solutions, databases, reporting systems, and analytical applications for DSNY. Performs data profiling, source-to-target mapping, data quality assessment, ETL development, documentation, and enterprise data architecture work. Collaborates with business, technical, and external stakeholders to support operational reporting, analytics, data governance, interoperability, and reliable IT systems across multiple platforms.

Design and optimize scalable cloud data warehouses, data lakes, pipelines, and Operational Data Stores for near-real-time IoT analytics. Integrate and process large datasets using Databricks, Snowflake, Flink, Spark, Kubernetes, and Iceberg. Monitor pipeline health, improve performance and cost efficiency, ensure data quality and security, and collaborate with technical and business stakeholders.

Build and maintain data infrastructure, ingestion pipelines, data marts, governed BI models, and Looker dashboards. Ensure data quality, platform performance, lineage, access controls, and consistency across business entities. Collaborate with technical and operational teams, support AI solution integration, train users, document best practices, and lead complex analytics projects using Agile methods.

Leads data engineering within an agile team, designing secure and scalable data platforms, pipelines, architectures, storage, access, analytics, governance, backup, and recovery strategies. Develops advanced data models, evaluates security controls, documents best practices, and drives responsible AI-assisted engineering adoption. Provides technical leadership through architecture decisions, mentoring, cross-team influence, and operational support using Python, Databricks, Spark, AWS, and modern lakehouse patterns.

Designs and maintains enterprise data architectures and ETL pipelines supporting mission-critical GEOINT systems. Assesses geospatial technologies, workflows, and processes; contributes to GIS strategy and technology roadmaps; develops architecture views; documents production processes and performance metrics; identifies infrastructure and performance improvements; and supports lifecycle, configuration, and change management for federal mission systems.

Build and support Python data applications, migrate systems to Azure, develop ETL pipelines, manage data lake resources, and design scalable hybrid cloud architectures. The role also involves implementing statistical and machine learning models, improving data science infrastructure, documenting solutions, and supporting analytics databases.

Designs, builds, and supports enterprise data products, analytics platforms, scalable pipelines, Lakehouse architectures, and integration solutions. The role integrates operational and enterprise systems, develops governed and reusable data products, supports reporting and analytics, and implements data quality, validation, monitoring, lineage, and modeling processes. Strong Databricks, SQL, Python, Azure, and Power BI expertise is required, with experience in operationally intensive environments such as manufacturing, IoT, supply chain, or logistics.

Builds and maintains enterprise data warehouse solutions, relational database schemas, ETL pipelines, and automated data processes. Responsibilities include optimizing complex SQL queries, indexing, execution plans, data modeling, governance, system support, and cross-functional delivery. Requires strong SQL, SSIS, data warehouse, and relational database expertise, with preferred experience in Azure Data Factory, ActiveBatch, and Snowflake.

Develops and maintains enterprise data warehouse solutions, relational database schemas, ETL pipelines, and automated data processes. Optimizes complex SQL queries, indexing, and execution plans for large datasets while supporting scalable data systems. Collaborates with analysts, product managers, QA, developers, and business stakeholders throughout solution design and delivery. Requires advanced SQL, data modeling, governance, SSIS, and data warehouse experience, with Azure Data Factory, ActiveBatch, and Snowflake preferred.

Designs, develops, and maintains scalable ETL/ELT pipelines and data platform capabilities for high-volume healthcare data. Responsibilities include complex SQL development, Airflow orchestration, dbt transformations, data quality frameworks, pipeline monitoring, troubleshooting, performance optimization, and documentation. The role collaborates with Data Operations, Product, Analytics, Data Science, and clinical teams to deliver reliable data for analytics, reporting, product features, and AI/ML initiatives.