Top Data Engineer Jobs

Design, develop, deploy, and support scalable data pipelines and data products. Collaborate with business and technology partners to shape solution architecture, optimize performance and cost, and promote reusable patterns. Support analytics, BI, and data science use cases while staying current on emerging data engineering and AI-assisted development technologies.

Design, develop, operationalize, and maintain enterprise-scale data applications and relational databases. Build scalable ETL/ELT pipelines, parse structured and unstructured data, and support data platforms on public cloud. Train, test, deploy, and maintain ML models, collaborate with interdisciplinary teams, and support mission-focused analytics for military and intelligence customers.

Design, build, and maintain scalable ETL/ELT pipelines (batch and streaming); manage data catalogs and API integrations with OAuth2; collaborate with cross-functional teams to ensure data quality and platform reliability for mission-driven projects.

Design and build scalable backend services and data ingestion pipelines, perform data modeling and large-file processing, develop reusable APIs, and optimize systems for speed and scalability while collaborating in an Agile team.

Build, maintain, and optimize reproducible data pipelines and large-scale relational databases for health disparities research. Collaborate with scientists to prepare, clean, transform, and integrate multi-source data, optimize pipeline performance, and produce datasets and visualizations for analysis and consortium distribution.

Lead design and implementation of a next-generation lakehouse data platform and high-scale batch/near-real-time pipelines. Own platform architecture, data modeling, governance, observability, and delivery to support BI, experimentation, and ML. Provide hands-on technical leadership, mentor engineers, and drive modernization toward an AI-first data engineering practice.

Design, implement, and deliver scalable data pipelines and integrations for government customers. Work with structured and unstructured data, APIs, databases, and cloud infrastructure (AWS). Collaborate with engineering, product, and data science teams to customize TRSS solutions, automate workflows, and provide solution architecture and operational support on-site in the Washington, DC area.

Design, build, test, and maintain cloud-based data pipelines and ETL processes using Python, Databricks and Spark. Develop SQL queries and dashboards, automate workflows, integrate code with GitHub/GitLab CI/CD, troubleshoot data quality, and collaborate with stakeholders to deliver scalable, secure data solutions.

Design, build, and maintain core data infrastructure and automated end-to-end pipelines to ingest, process, store, and transform data. Develop data models and schemas, implement processing frameworks, enforce data quality and lineage, troubleshoot pipelines, collaborate with data scientists and security teams, document architecture, and support data governance and continuous improvement.

As a Data Platform Engineer, you'll architect and implement data services, build data pipelines, optimize for real-time risk assessment, ensure data governance, and collaborate across teams.

The Data Engineer III will design, build, and optimize data pipelines and backend systems, integrating with SAP and supporting analytics applications. Responsibilities include developing ETL workflows, backend applications, and ensuring data quality.

Lead Data Engineer responsible for building technical solutions, collaborating with Agile teams, utilizing big data technologies, and developing cloud-based data warehousing solutions to enhance user experiences.

Lead architecture and delivery of scalable data infrastructure: design and optimize ETL/ELT pipelines, enterprise data models, and data quality frameworks. Define platform standards, mentor engineers, drive cross-functional initiatives, and partner with analytics, data science, and engineering stakeholders to ensure reliable, production-grade data assets.

Build and maintain data pipelines and ETL processes using Python and Databricks, manage relational databases, support ML/AI deployments, collaborate with engineers and data scientists, and ensure data availability and scalability for DoD/IC customers.

Build and maintain ETL pipelines using Python and SQL, integrate and transform data from diverse sources, implement quality checks and monitoring, optimize data structures for scalability, and provide Palantir technical guidance to support analytic teams and mission-driven intelligence workflows.

As a Data Engineer, you will design and implement data pipelines, analyze requirements, and develop software solutions to drive operational efficiency.

Design and maintain SQL Server databases and T‑SQL stored procedures; build and monitor ETL/ELT pipelines; troubleshoot and performance‑tune queries; collaborate with software engineers and data scientists; gather stakeholder requirements; monitor database health and respond to failures; learn new skills as needed. Fully on‑site at St. Peters, MO.

Design, develop, and optimize data warehousing and ETL/ELT solutions on Microsoft SQL Server and cloud platforms. Perform data profiling, cleansing, dimensional modeling (Kimball), and performance tuning. Build semantic models and Power BI/Microsoft Fabric solutions, advise on architecture, mentor teammates, assist in pre-sales/scoping, and promote BI best practices across clients.

The Data Engineer will architect and maintain data pipelines, manage Snowflake environments, ensure data quality, and mentor junior engineers. Responsibilities include designing ELT pipelines and transforming data using dbt.

The Data Engineer will develop and maintain data pipelines, perform backend integrations, and enhance data systems for effective reporting and analytics.

Build, operate, and optimize data platforms and pipelines (Snowflake, Azure ESB). Manage ETL, data migrations, ingestion, quality, and analytics tools (Cognos, Tableau). Collaborate with IT and business stakeholders, enforce data governance, support regulatory and cybersecurity requirements, and provide advanced support and system integration for banking APIs.

Design and build large-scale ETL/ELT pipelines and analytical architectures on GCP. Assess, plan, and drive enterprise data and cloud migration programs, modernize legacy systems, optimize query performance, and enable scalable cloud-native solutions for clients.

Design, build, and maintain scalable cloud data pipelines and platform components to power analytics, ML, and applications. Implement transformations, data quality checks, lineage and governance, optimize performance and cost, monitor platform health, and build reusable components across business units while collaborating with product and architecture teams.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Lead pre-sales data engineering engagements to design and implement scalable lakehouse data platforms (Snowflake/Databricks). Advise customers on data strategy, governance, pipelines, AI-ready foundations, migrations, and translate technical solutions into business outcomes while supporting sales and partner enablement.