Top Data Engineer Jobs in Washington, DC

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business insights.

Lead design and implementation of scalable data infrastructure, pipelines, and transformations using cloud platforms. Build and optimize data warehouses (Snowflake, Databricks), orchestrate workflows (Airflow, Hadoop), ensure data quality and security, and coach teams to deliver analytics solutions that drive tax practice insights and client outcomes.

Design and build data infrastructure, pipelines, and transformations using Azure Data Factory, Azure Databricks, and Snowflake. Develop data architecture and modeling, maintain data lakes/warehouses, ensure data quality and security, optimize performance, and collaborate with clients to deliver tailored data solutions and governance.

Develop and maintain cloud-based data pipelines, integrations, and ETL/ELT processes supporting analytics and operational reporting. Build Snowflake and Azure solutions using Databricks and Storage services, assist with cloud migrations, and support API and MuleSoft integrations. Troubleshoot pipeline issues, ensure data quality and governance, participate in testing and code reviews, and collaborate with technical and business stakeholders. The role also requires documentation, secure data practices, and continuous learning.

Builds and operates scalable Azure data pipelines, storage, datasets, and data products for analytics and operational insights. Responsibilities include Azure Data Factory development, Blob Storage and Azure SQL optimization, dimensional and Data Vault modeling, data quality and governance, security, monitoring, troubleshooting, CI/CD, and performance optimization. Collaborates with analytics, product, and operations teams to deliver reliable, documented data solutions. Desired experience includes Synapse, Fabric, Power BI, streaming technologies, infrastructure as code, and data cataloging.

Build and optimize batch and streaming data pipelines on Google Cloud for analytics and machine learning. Develop Python, SQL, Beam, and Spark transformations; deliver curated and ML-ready datasets; implement data quality, governance, security, monitoring, and schema management; optimize BigQuery; maintain Airflow DAGs; and manage Terraform-based infrastructure and CI/CD. Collaborate with architects, data scientists, ML engineers, and stakeholders while troubleshooting pipeline and data quality issues.

Build and optimize batch and streaming data pipelines for analytics and AI workloads using GCP services, Python, SQL, Beam, and Spark. Develop ML-ready datasets, implement data quality, governance, security, monitoring, and schema management, and optimize BigQuery performance. Maintain Airflow DAGs, Terraform infrastructure, and CI/CD pipelines. Troubleshoot production issues and collaborate with architects, data scientists, ML engineers, and stakeholders. The role requires U.S. citizenship, a bachelor’s degree, Public Trust eligibility, and 3–6+ years of experience.

Develops data algorithms and systems, analyzes structured and unstructured sources, maintains data management tools, and applies statistical, qualitative, and quantitative methods to deliver actionable insights. Collaborates with clients in data-rich environments to interpret information and support informed decisions. The role requires R or Python, data cleansing and parsing, scientific analysis methods, and an active TS/SCI clearance with CI polygraph.

Build and own scalable data platforms and ingestion pipelines using BigQuery, dbt, and modern ingestion tools. Design batch and CDC workflows, improve reliability and observability, implement governance and access controls, and support analytics and AI agents. Develop Python services and internal tooling, contribute to architecture and lakehouse experiments, collaborate with stakeholders, and mentor engineers. The role requires strong SQL, Python, cloud infrastructure, testing, CI/CD, and AI-assisted development experience.

Lead data architecture and database operations for ACWS. Develop and maintain data pipelines, mappings, integrations, and reporting feeds; support migration, transformation, validation, reconciliation, and data quality management. Coordinate requirements across BI, application, architecture, and database teams. Document data flows and transformation logic, and recommend improvements to reliability, automation, analytics readiness, and governance.

Designs and maintains scalable data pipelines and platforms using Databricks and cloud technologies. Responsibilities include ETL development, data modeling, automation, CI/CD, lakehouse implementation, dashboard enhancement, and advanced analytics using LLMs and AI to improve grant oversight and detect fraud, waste, and abuse. The role collaborates with architects, stakeholders, and federal government clients to support data-driven decision-making.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Design, build, and optimize scalable data pipelines, warehouses, lakes, and processing systems for analytics and business intelligence. Ensure data quality, security, reliability, and cost efficiency while collaborating with data scientists, analysts, and engineers. Maintain technical documentation, support Databricks solutions, and apply emerging data engineering technologies. The role requires strong programming, SQL, ETL, orchestration, data modeling, cloud, and big data expertise.

Architects and leads enterprise data engineering platforms, including scalable ETL/ELT pipelines, streaming frameworks, cloud infrastructure, data warehouses, and AI/ML data systems. Establishes engineering standards, governance, monitoring, data quality, security, and schema management practices. Designs AI agents using LLMs, partners across business and infrastructure teams, and makes high-impact architectural decisions for reliable, secure, multi-region data environments.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Develop and maintain data transformation pipelines, data models, dashboards, visualizations, and self-service analytics tools. Clean, validate, and analyze complex datasets; translate business needs into technical solutions and actionable insights; document workflows; and communicate findings to technical and non-technical stakeholders. The role requires Kibana expertise, strong ETL and data pipeline knowledge, collaboration skills, an active TS/SCI clearance, and Security+ certification.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Designs, builds, tests, and maintains scalable data infrastructure and ETL/ELT pipelines for mission partners. Uses AWS Glue, Glue Catalog, and Step Functions to integrate, migrate, transform, and validate data. Develops data models, schemas, queries, and production-ready datasets while collaborating with analytics and business teams. The role may also support AI/ML pipelines, data governance, DevOps workflows, Databricks, and real-time streaming architectures.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

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 and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.