Top Senior Data Engineer Jobs in Washington, DC

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Designs and optimizes Power BI dashboards, semantic models, datasets, DAX measures, and reporting solutions for identity governance and administration analytics. Partners with ETL engineers and stakeholders to validate Parquet data from Azure Synapse and SailPoint sources, establish data-quality checks, monitor refreshes, define KPIs, and document data lineage. Supports compliance reporting, access governance, audit readiness, certification metrics, and Zero Trust analytics.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Designs and productionizes hybrid knowledge graph solutions for healthcare and clinical data using LPG and RDF technologies. Builds FHIR transformation and ingestion pipelines, implements ontology mappings, entity linking, identifiers, provenance, validation, and graph queries with Cypher or SPARQL. Collaborates with ontology, terminology, FHIR, clinical, and data engineering teams, while contributing to architecture reviews, code reviews, testing, and engineering best practices.

Build customer-facing data products for the single-family rental market. Responsibilities include architecting real estate data models, designing reliable data pipelines and workflows, shaping data platform architecture, establishing engineering standards, and developing real estate domain expertise. The role partners with data science and product teams and requires leadership in scalable data engineering.

Designs and implements scalable data pipelines, cloud data platforms, storage solutions, and analytics infrastructure. The role focuses on Snowflake and AWS architecture, migrating legacy systems to cloud environments, developing Python and SQL data-processing logic, orchestrating workflows, and implementing Infrastructure-as-Code and CI/CD. The engineer also contributes to data governance, quality, observability, stakeholder communication, mentoring, and technical leadership.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Designs, builds, and maintains scalable batch and streaming data pipelines across AWS and GCP. Develops ETL/ELT workflows using Python, PySpark, and advanced SQL; optimizes performance, storage, and query execution; implements data quality and governance controls; and supports data lakes, warehouses, and ML workflows. Collaborates with data scientists and ML engineers on MLOps automation and production deployments while maintaining CI/CD, Docker, infrastructure standards, and technical documentation.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Lead enterprise data engineering efforts supporting defense missions. Responsibilities include prioritizing technical tasks, mentoring engineers, conducting code reviews, translating government requirements into scalable data architectures, developing Databricks ETL/ELT pipelines with Python, PySpark, and SQL, troubleshooting pipeline performance and reliability, and documenting systems. The role collaborates with government stakeholders, product owners, data scientists, analysts, and technical teams to deliver secure, high-throughput data platforms and actionable insights.

Designs and maintains scalable Azure data pipelines and ETL/ELT solutions across data lakes, warehouses, lakehouses, streaming platforms, and enterprise sources. Builds integrations using Azure Data Factory, Synapse, Databricks, Spark, Kafka, and related services; applies security, governance, monitoring, and performance optimization practices. Develops Power BI dashboards, reports, data models, and visualizations, while supporting analytics and AI/ML use cases. Collaborates in Agile teams and automates deployments through DevOps and CI/CD practices.

Designs and operates data infrastructure for a clinical trial platform. Builds pipelines that extract, normalize, validate, and link information from clinical documents and publications into Aurora and GraphDB. Develops embeddings, vector search, RAG workflows, data-serving APIs, lineage tracking, and regulatory audit trails. The role also requires AWS-based orchestration, clinical data quality controls, biomedical knowledge graph modeling, and deployment automation using infrastructure-as-code and containerization.

Designs and delivers backend systems, APIs, data models, ETL workflows, data interactions, and user interfaces. Optimizes data operations for scalability, performance, integrity, and security while contributing to automated testing and CI/CD. Requires strong Python and Apache Spark experience, distributed computing knowledge, Agile experience, and data analysis skills. The role supports healthcare-focused digital services in a remote-first environment.

Lead Cohere’s Data Foundation pillar, managing a team responsible for the full data lifecycle. Own the roadmap, BigQuery warehouse, Airflow and Cloud Run orchestration, dbt modeling, Looker and API serving, Terraform infrastructure, governance, access controls, reliability, incident response, and data freshness SLAs. Lead development of an agentic analytics platform and evaluation framework, establish company-wide analytics standards, and partner with product, engineering, security, legal, and business teams.

Data Engineer II will modernize BlueLabs’ data platform by leading Kimball dimensional modeling and legacy warehouse migrations, building reliable production data pipelines, and enforcing data quality standards. The role requires advanced SQL, Python, and dbt expertise, hands-on Snowflake and Redshift experience, and ownership of testing, documentation, observability, and scalable architecture. The engineer will collaborate cross-functionally, guide Data Engineer I team members, contribute to architectural decisions, and translate business logic into maintainable, cloud-agnostic data layers.

Build and operate a Kubernetes-based data platform spanning lakehouse storage, ingestion, transformation, entity resolution, lineage, and data quality. Develop batch and streaming pipelines across heterogeneous and disconnected environments, create full-stack tools for subject matter experts, and expose documented APIs for applications and AI agents. Collaborate with product, AI/ML, infrastructure, mission partners, and field teams while mentoring engineers and supporting secure, air-gapped deployments.

Designs, builds, and optimizes scalable data pipelines, ETL workflows, integrations, and reporting solutions. Develops SQL, Python, and Spark data-processing solutions using Azure Data Factory and Microsoft Fabric, while monitoring production reliability and performance. Collaborates with stakeholders, participates in code reviews, documents technical solutions, mentors junior engineers, and improves engineering practices. Requires strong data modeling, cloud data platform, Git, communication, and problem-solving skills.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Architect and lead enterprise data platforms, pipelines, and infrastructure using Spark, Databricks, and cloud technologies. Build scalable data models and dimensional schemas, establish governance and data quality frameworks, optimize performance and reliability, and implement monitoring. Partner with stakeholders to translate requirements into technical solutions, lead design reviews, mentor engineers, contribute to platform strategy, troubleshoot complex failures, and support hiring and team development.

Leads the architecture, development, and maintenance of scalable batch and real-time data pipelines, cloud data platforms, ETL/ELT workflows, and ML data infrastructure. Establishes standards for data quality, observability, lineage, governance, security, and modeling. Partners with data scientists, engineers, DevOps, and business stakeholders; evaluates technologies, drives technical initiatives, mentors engineers, and supports production-grade APIs and machine learning pipelines.

Designs, builds, maintains, and optimizes scalable data pipelines and ETL/ELT workflows using Azure Databricks, SAS, SSIS, SQL, Python, and Spark. Integrates data from internal and external sources, improves performance and reliability, troubleshoots complex processing issues, and supports cloud-based data platform architecture. Collaborates with engineering, analytics, product management, SRE, and other stakeholders to deliver high-quality data solutions.

Own end-to-end production data platforms, writing Python and SQL, designing AWS pipelines, modeling analytics data, and implementing quality and reconciliation controls. Make architecture decisions independently, ensure correctness and auditability, and use AI coding tools effectively. The role also supports applied AI initiatives including entity resolution, LLM-assisted analytics, embeddings, and anomaly scoring.

Owns the end-to-end data platform, including Snowflake infrastructure, ingestion pipelines, Airflow orchestration, dbt transformations, governed gold-layer models, and metric definitions. Ensures reliability, performance, testing, documentation, governance, and CI/CD standards. Builds data systems from scratch, partners with analytics stakeholders, and uses AI-assisted development tools to accelerate engineering work. Requires strong SQL, platform ownership, software engineering practices, and experience with Snowflake, Airflow, and dbt.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Build and optimize data transformation pipelines using SQL, dbt, Airflow, Python, BigQuery, and relational databases. Collaborate with stakeholders and data engineers, manage task scheduling, resolve data issues, perform analyses, present recommendations, support data exploration tool adoption, and mentor team members. The role also contributes to hiring, onboarding, and professional development while ensuring data accuracy and insight quality.