Top Senior Data Engineer Jobs

Designs and optimizes scalable data pipelines and architectures for Department of Defense missions, AI-enabled use cases, and operational analytics. Integrates complex data sources while ensuring quality, reliability, security, governance, and compliance. Builds ETL processes and data models, collaborates with data scientists and mission stakeholders, and supports deployment and sustainment of data platforms in secure DoD environments.

Leads the design, development, administration, and optimization of enterprise data pipelines and Microsoft Fabric data platforms. Responsibilities include ETL/ELT architecture, medallion layers, data quality, CI/CD, infrastructure as code, security, hybrid connectivity, disaster recovery, performance optimization, and troubleshooting. The role also mentors engineers, establishes technical standards, supports architecture reviews, and provides senior-level guidance for secure, scalable, reliable data platform implementations.

Design and build end-to-end data pipelines and integrations on Databricks, implement medallion architectures, develop streaming and NiFi pipelines, integrate purpose-built stores, operationalize ML/MLOps (training, deployment, monitoring), and explore LLM/RAG generative AI solutions while enforcing data governance, security, and performance for large-scale analytics.

Design and maintain petabyte-scale data architectures and ETL pipelines; enable automated MLOps workflows; lead teams building ML systems and backend services; implement CI/CD and DevSecOps practices; integrate components, write maintainable Python code, and produce technical documentation while collaborating with cross-functional teams.

The Data Engineer will analyze data, optimize systems, and collaborate with data scientists and engineers, requiring strong SQL and PowerBI skills.

The Senior IT Data Engineer will design and implement enterprise data architectures, develop scalable ETL/ELT pipelines, and support data-driven initiatives for the Federal Reserve Board.

The Principal Data Engineer will design and implement scalable data platforms, lead architecture discussions, and mentor teams. Requires expertise in Azure and Databricks.

Design and implement scalable, secure enterprise geospatial database solutions using Esri technology. Collaborate across project phases to configure, maintain, tune, secure, and automate databases, support ArcGIS applications, and provide technical guidance for migrations, integrations, and operational readiness for Defense/Intel clients.

Consult with customers to design and implement enterprise geospatial database solutions using Esri/ArcGIS technologies. Configure, secure, monitor, and automate databases (backup/HA/DR, performance tuning, data loading). Collaborate across teams, provide technical guidance, and support database migrations and integrations for mission-critical defense and intel applications.

Consult with customers to design and implement scalable, secure enterprise geospatial database solutions using Esri/ArcGIS technologies. Configure and maintain database environments (backup, HA/DR), monitor and tune performance, implement security and access controls, automate data workflows, and provide technical guidance across requirements, design, build, and deployment phases. Support integration with ArcGIS web, desktop, and mobile applications for Defense and Intel clients.

The Data Engineer will analyze data quality, design prediction algorithms, collaborate with the engineering team, and generate business insights.

The Data Engineer will develop data pipelines and integrations, focusing on healthcare data to enhance financial operations, ensuring efficient data handling and driving impactful solutions.

The Data Engineer will gather and process large datasets, develop algorithms, conduct data analysis, and improve predictive models for business decisions.

Design and implement data integration solutions, build data pipelines, manage cloud resources, and develop real-time data flows using AWS technologies and Snowflake.

Lead the design and evolution of the enterprise data platform, establish standards, mentor teams, and collaborate across departments to deliver high-quality data products.

The role involves designing and scaling data infrastructures for private equity, developing ETL/ELT pipelines, ensuring data quality, and collaborating with various teams on data governance.

The Cloud Data Engineer will design and develop data warehouse solutions, create efficient data pipelines, collaborate with teams, and monitor system health.

Design, build, test, and operationalize data pipelines and solutions using cloud technologies. Ensure data quality, governance, and security; analyze sources; resolve defects; collaborate across teams; support AI/ML and BI use cases; and document and educate users on data products.

Design, build, and maintain petabyte-scale ETL pipelines and data platforms using Spark (PySpark/Java), AWS, and Kafka. Model complex data structures, convert varied data formats, operationalize and monitor scalable data applications, and lead cross-functional teams to support mission-driven analytics. Must meet TS/SCI with polygraph requirements.

Administer, maintain, and optimize SQL and NoSQL database environments; apply data warehousing concepts; perform data management and maintenance using SQL, Python, and Apache Spark; support cloud database infrastructure with Git, AWS, or Azure; and provide database support for Tableau and Power BI reporting.

Administer, maintain, and optimize SQL and NoSQL database environments. Apply data warehousing and data management practices, using SQL, Python, and Apache Spark. Support database infrastructure with Git and AWS or Azure, and provide database support for Tableau and Power BI reporting and visualization tools.

Investigate and resolve complex data quality, integration, and reconciliation issues across ERP, e-commerce, databases, ETL pipelines, warehouses, and reporting systems. Trace discrepancies to their root causes, remediate and backfill data, improve master-data mappings, automate reconciliation and monitoring, document lineage and dependencies, and establish stronger controls. The role requires independent production troubleshooting, deep SQL Server and T-SQL expertise, cross-functional collaboration, and the ability to improve fragile legacy data processes.

Develops and manages data pipelines with Snowflake, SQL, dbt, and Airflow; builds REST APIs and microservices using Python and FastAPI; supports Salesforce Data Cloud; implements CI/CD; creates reusable Slack app frameworks; and collaborates cross-functionally on scalable solutions.

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Build, optimize, and maintain data pipelines, ETL processes, data architectures, AI solutions, and analytics platforms. Develop cloud-based solutions using Azure Functions and containerization, implement CI/CD workflows, integrate databases and APIs, and ensure data quality, monitoring, reliability, and documentation. Troubleshoot production issues, collaborate with cross-functional teams, and apply modern data engineering, AI, cloud, and DevOps practices.