Top Data Engineer Jobs in Washington, DC

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Designs and maintains healthcare data pipelines, ETL workflows, databases, data models, and analytical frameworks. Analyzes claims and payer data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders and collaborates with healthcare policy experts, analysts, and other stakeholders to translate requirements into data solutions. The role also improves data infrastructure and analytics capabilities.

Designs and delivers analytics solutions on Snowflake using dbt, SQL, and modern ELT practices. Responsibilities include data modeling, pipeline development, migrations, performance optimization, data quality, security, orchestration, CI/CD, and release management. Leads discovery and requirements gathering, translates business needs into analytics architectures, provides technical guidance, and communicates recommendations to client stakeholders in agile consulting environments.

Architect and operate BILL’s enterprise data platform across ingestion, lake storage, batch and streaming processing, query, feature store, knowledge graph, and search layers. Drive platform-wide technical decisions, migrations, standards, reliability, cost efficiency, and self-service capabilities. Partner with ML, Risk, Payments, and Analytics teams to deliver scalable solutions, lead complex initiatives from inception through production, and mentor senior and staff engineers.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, and data warehousing environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, enables reporting and AI/ML workloads, and partners with business and IT teams to deliver technical data solutions.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies data warehousing practices, and partners with business users, analysts, and IT teams to deliver reporting, analytics, and AI/ML solutions.

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.

Design, build, and maintain scalable Snowflake data pipelines and workflows. Ingest, transform, and deliver data from EHRs, claims, APIs, and CRM systems into analytics-ready structures. Optimize Snowflake performance and costs through warehouse sizing, query tuning, data modeling, and security design. Implement orchestration, DevOps, version control, and CI/CD practices while collaborating with client architects and analysts. The role also involves explaining technical tradeoffs, supporting repeatable deployments, adapting to unfamiliar challenges, and mentoring others.

Build and maintain IICS ETL/ELT pipelines, develop Amazon Redshift data models, and use SQL and Python for data transformation and automation. Create Power BI reporting models, implement data quality checks, document pipelines, and collaborate with client stakeholders and engineering teams. The role requires autonomous work, strong communication, and the ability to translate technical solutions into business context, with bonus value for Veeva or life sciences experience.

Build and maintain modern data-centric applications, pipelines, transformations, enrichment processes, provisioning layers, and user interfaces supporting clinical and operational healthcare processes. Analyze clinical data and report trends using cloud, big data, data science, and modern software development practices. Collaborate with Product, Platform, and Architecture teams, mentor Data Engineers, document solutions, manage source control, and research emerging technologies.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Designs and scales cloud-native data platforms and ingestion pipelines using AWS, Databricks, and Spark. Builds APIs, file-upload mechanisms, ETL/ELT workflows, and Lakehouse architectures; supports SQL Server data loading and legacy C# .NET processes. Partners with analytics, software engineering, architecture, and product teams to ensure data quality and availability for reporting and machine learning. Troubleshoots pipelines and applies governance, security, compliance, documentation, and reliability best practices.

Build and manage end-to-end marketing data platforms on Google Cloud. Responsibilities include configuring GA4 and Google Tag Manager, creating BigQuery ingestion pipelines, managing Cloud Functions, Pub/Sub, IAM, and service accounts, developing LookML semantic models and dashboards, and orchestrating ETL and reverse ETL workflows. The role also supports CDP integrations, identity resolution, audience syncing, experimentation, data quality assurance, and lifecycle marketing analytics.

Architect, build, and operate low-latency market data systems, including feed handlers, normalization, distribution, and exchange connectivity. Design high-throughput infrastructure for real-time trading data, improve reliability and performance, participate in observability, on-call, and incident response, and deliver well-tested features end to end. Partner with engineering, product, and cross-functional stakeholders while leading a lean, high-impact team.

Design and deliver modern data platforms, scalable and secure ingestion and transformation pipelines, durable data models, streaming systems, and reporting layers. Partner directly with clients and business stakeholders to translate requirements into architecture, explain tradeoffs, and own delivery outcomes. The role includes production implementation across cloud data platforms, orchestration, governance, real-time technologies, and analytics or AI data consumption.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Build and maintain commercial data pipelines, transformations, models, integrations, and data products in Snowflake. Responsibilities include data modeling, quality validation, monitoring, troubleshooting, backfills, operational automation, documentation, and stakeholder collaboration. The role supports analytics, reporting, Streamlit, and AI-enabled use cases while maintaining reliable production workloads, privacy controls, and engineering standards.

Lead the design and operation of a scalable data platform, owning ingestion, orchestration, observability, lineage, data modeling, contracts, and ETL/ELT pipelines. Build Databricks and PostgreSQL solutions, implement data-quality controls, anomaly detection, governance, privacy, and compliance practices, and establish reusable engineering standards. Mentor engineers and ensure reliable, traceable data across markets and product lines.

Own and evolve Worldly’s production data lake, Postgres warehouse, CDC and streaming pipelines, dbt transformations, orchestration, BI access controls, and graph integrations. Maintain reliable, governed analytics infrastructure using Iceberg, Trino, Dagster, Terraform, and AWS. Support production generative AI workflows involving embeddings, similarity search, extraction, and classification. Responsibilities include data quality, monitoring, security, incident response, disaster recovery, schema governance, and collaboration with data science and analytics stakeholders.

Designs, administers, and optimizes data systems supporting FSA IAM applications. Develops migration plans from legacy AIMS and PAS systems, creates data models, mappings, layouts, dictionaries, and integration processes, and supports analytics and fraud detection. Manages database configuration, performance tuning, monitoring, backups, capacity, secure data handling, encryption, network data flows, token management, and documentation while collaborating with development, security, and operations teams.

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.

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.

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.

Manage, design, and optimize data activities for federal IAM systems. Develop data models, file layouts, source-to-target mappings, data dictionaries, integrations, ETL processes, and analytics support for identity analysis and fraud detection. Administer and tune databases, including configuration, backups, monitoring, capacity planning, and performance optimization. Support secure handling of identity and PII data, network-related data flows, documentation, token management processes, and collaboration across development, security, and operations teams.