Top Data Engineer Jobs in Washington, DC

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Build and maintain centralized pricing data infrastructure, scalable workflows, and data pipelines; integrate AI capabilities; deliver pricing tools, reporting, and governance to support data-driven pricing and budgeting across the firm.

Design, build, and maintain AWS-integrated data pipelines and infrastructure. Translate business needs into schemas and data dictionaries, optimize for scalability, ensure data governance, troubleshoot stakeholders' issues, and provide Tier 3 support for deployed applications and dataflows.

Maintain and monitor data pipelines that aggregate healthcare claims for the Cost Roll process. Validate and reconcile claim and provider demographic data, troubleshoot data issues, perform data quality checks, and collaborate with stakeholders to ensure reliable downstream financial and clinical systems.

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.

The Data Engineer will develop data and software solutions for law enforcement, create reports, and manage data sources. Responsibilities include collaborating with teams to analyze data, communicate with clients, and ensure high-performance analytics tools.

Build and maintain greenfield data infrastructure and pipelines (ingest, validate, organize) for basketball and performance datasets. Implement medallion architectures, data validation, and optimizations; collaborate with cross-functional stakeholders to deliver analytic datasets for web and mobile applications.

Build and maintain AWS cloud-native data ingestion pipelines and ETL using Lambda, Glue, S3, ECS/Fargate and Redshift. Implement SQL-based data quality checks, monitor jobs via CloudWatch, and deliver dashboards in QuickSight/Tableau/Power BI to support stakeholders.

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.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

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.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

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

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Design, build, and maintain Data Vault 2.0 data warehouse and Power BI solutions. Develop ETL pipelines, data models, reusable datasets, and data governance. Perform profiling, cleansing, migrations, and collaborate with infrastructure teams to optimize data delivery, automation, and reporting.

Own and maintain the companys data infrastructure and pipelines. Design, build, and monitor ETL/ELT workflows and data architecture; integrate SaaS sources; enforce data quality, standards, and governance; partner with stakeholders to deliver analytics-ready data and meet data availability SLAs.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Design, build, and maintain scalable end-to-end data pipelines and transformation workflows using Databricks, Spark/PySpark, and cloud services. Integrate diverse data sources, implement validation/monitoring, support analytics and reporting, and enable automation, governance, and security in an AWS cloud-native environment.

Design, build, and maintain scalable end-to-end data pipelines using Databricks, Spark, and cloud services. Develop transformation workflows, integrate diverse data sources, enforce data quality, monitoring, and observability. Leverage AWS (S3, Glue, Lambda, Redshift) and cloud warehousing; collaborate with data science, analytics, and business teams. Contribute to governance, security, and automation initiatives.