Top Data Engineer Jobs in Washington, DC

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Own and modernize Upsides analytics data platform: migrate pipelines, reduce cost, improve governance, design reusable modeling/orchestration patterns, deliver domain-critical data products, lead cross-functional initiatives, mentor engineers, and support ML and product teams.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

Build and maintain reliable data pipelines, Airflow DAGs, and Snowflake-based Data Vault/warehouse models. Implement CI/CD, automated testing, observability, and production support while partnering with stakeholders and developing insurance domain expertise.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

The Staff Data Engineer will lead design and implementation of analytics platforms and data products, collaborate cross-functionally, mentor engineers, and drive best practices at Upside.

Senior Addepar data engineer/analyst responsible for supporting and scaling Addepar integrations, building and maintaining data pipelines (Addepar↔Snowflake/Canoe/other platforms), diagnosing and resolving data quality issues, developing SQL-based reconciliations and automation, partnering with business and technology stakeholders, and supporting production operations, governance, testing, and documentation for wealth and asset management data.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Builds and maintains ETL and database pipeline architectures, automates data collection and validation, supports data modeling and reporting, collaborates with stakeholders and data science to integrate models, coaches junior staff, and implements governance and technical solutions for large datasets.

Design, build, and optimize end-to-end data pipelines and architectures on cloud infrastructure (AWS). Implement ETL/dataflow artifacts, support stakeholders, provide Tier 3 support, automate processes for scalability, and collaborate on product vision and roadmaps while supporting off-hours deployments as needed.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms using distributed tools and cloud services. Mentor cross-functional teams, oversee data architecture and modeling, process structured and unstructured data, and support analytics and mission-driven projects while meeting security clearance requirements.

As a Data Engineer, you will develop and maintain scalable data platforms and pipelines, support analytics, and work with cross-functional teams to organize data for actionable insights.

Develop and design data pipelines, optimize data delivery and automate processes to support end-to-end solutions in various settings.

The Data Engineer role involves developing and maintaining data pipelines for forecasting in commercial real estate, ensuring data integrity and supporting analytics teams.

Design, build, and deploy scalable data pipelines and validation for government production environments. Establish and maintain schemas, optimize SQL, handle messy/disparate data sources, work with large (100GB+) workflows, use Databricks/Spark/Python, and support ML/predictive solutions while mentoring teammates and engaging with clients. Active Secret Clearance required.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Data Engineers at Rhombus Power design and implement data infrastructure, analyze and maintain data systems, and collaborate across teams. They develop code for data processes and ensure strong client connections.

Lead design, development, and implementation of data management, ETL, data pipelines, reporting, API integrations, and automation for Head Start programs. Provide technical leadership, project management, data governance, system performance oversight, stakeholder coordination, and continuous improvement to deliver scalable, secure solutions.