Top Data Engineer Jobs in Washington, DC

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

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.

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.

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.

As a Data Engineer at Edmunds, you will architect and maintain data platforms for analytics and AI/ML, ensuring scalable, reliable data processing and supporting cross-functional collaboration.

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

Builds and optimizes ETL/ELT pipelines, integrates disparate data sources, ensures data quality/security, supports analysts and data scientists, and implements data governance and metadata management.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms using PySpark and Azure Databricks. Parse and process structured and unstructured data, support platform scaling/monitoring/operation, and collaborate with multidisciplinary teams to deliver analytics that inform clinical and public health decision-making.

Design, build, and maintain scalable ETL/ELT pipelines and data platforms (Azure Databricks/PySpark) to process structured and unstructured data. Lead cross-functional teams, support analytical exploration, convert diverse data into usable formats, and communicate technical findings to clinical and public health stakeholders while meeting government vetting and Public Trust requirements.

Design, build, and maintain scalable data pipelines and ETL processes in Azure (Synapse, Data Factory). Integrate data via APIs, ensure data quality/security, support analytics and Power BI reporting, collaborate with stakeholders, and manage work using Agile/Jira to support federal healthcare and VA initiatives.

The Data Engineer designs and maintains scalable data solutions, builds data pipelines, ensures data quality, and collaborates with teams to support analytics.

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.

Design and optimize data pipelines for web scraping and processing data. Collaborate with ML engineers and develop APIs while preparing data for analysis.

The Data Engineer will develop software for government environments, manage data validation, maintain schemas, and work with large data workflows.

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 ETL pipelines and data infrastructure to support analytics and reporting. Write and schedule workflows (Airflow DAGs), manage third-party data ingestion, implement data validation, optimize performance, and collaborate on data models, governance, and ML model deployments.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Lead architecture and implementation of scalable data pipelines, integrations, and cloud data infrastructure. Own IaC (Terraform), workflow orchestration (near-real-time and batch), third-party API and AI/LLM integrations, data export pipelines, and internal tooling. Mentor engineers, drive technical strategy and cross-team priorities, and communicate tradeoffs to technical and non-technical stakeholders. Must work EST/CST hours and be authorized to work in the US.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

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.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.