Top Data Engineer Jobs in Washington, DC

Builds and maintains scalable data pipelines, ETL infrastructure, data warehouses, and analytics tools. The role assembles complex datasets, automates processes, optimizes data delivery architecture, and supports cross-functional data initiatives. Collaborates with executive, product, data, and design stakeholders to resolve technical issues and improve analytics systems. Requires proficiency in SAS, Python, Java, MATLAB, SQL, NoSQL, Hadoop, MapReduce, Hive, Presto, UNIX, and Linux, with JavaScript, R, and basic machine learning preferred.

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.

Design, build, and optimize end-to-end data pipelines and dataflows, integrate with AWS, maintain schemas/data dictionaries, provide Tier 3 support, collaborate with stakeholders and product teams, document architectures, and support off-hours deployments. Ensure data governance, scalability, and production readiness while working primarily on-site in a cleared defense environment.

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.

Designs and maintains scalable cloud data architectures, pipelines, ETL processes, data warehouses, databases, and data models. Ensures data quality, accuracy, security, availability, and consistency across sources. Develops SQL queries for analytics and reporting, and documents pipelines, schemas, architectures, and models. The role requires expertise with Python, Java, Scala, R, SQL, relational and NoSQL databases, cloud platforms, and Snowflake.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Build, deploy, and maintain secure, scalable data pipelines and analytics on AWS/Databricks. Implement CI/CD, governance (Unity Catalog, lineage), platform observability, and operational support for enterprise datasets and Databricks SQL reporting.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Build and operate cloud data capabilities, including ingestion pipelines, transformations, data models, curated data products, and data-quality controls. Use Snowflake, dbt, SQL, and Python to deliver governed data for analytics, applications, automation, and AI workflows. Implement testing, CI/CD, documentation, lineage, access controls, monitoring, incident resolution, and performance optimization. Collaborate with engineering, analytics, governance, security, and business teams in an Agile, product-oriented environment with active AI-assisted development.

Design and operate scalable, cloud-native data pipelines and orchestration systems for imaging, biomarker, and healthcare data. Build Databricks and distributed processing workflows, integrate DICOM data into data lakes, and implement validation, observability, monitoring, and fault tolerance. Collaborate with data science, AI, R&D, product, and data management teams to deliver secure, compliant, reliable data for analytics and machine learning.

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.

Designs and operates an AWS-native lakehouse supporting analytics, reporting, visualization, and AI/ML. Builds batch and streaming ingestion pipelines using Python and PySpark, Apache Iceberg data structures, governance and metadata services, quality controls, security, observability, and CI/CD automation. Optimizes performance, scalability, reliability, and cost while collaborating with cross-functional technical teams and documenting architecture, operations, and secure configurations.

Designs and maintains data warehouses, semantic models, metrics layers, pipelines, dashboards, and certified datasets. Establishes KPI governance, data security, row-level access controls, quality standards, and observability. Builds Sigma, Power BI, and Databricks dashboards, supports self-service analytics, resolves BI issues, and enables AI/ML use cases. Collaborates with architecture, engineering, analysts, and business stakeholders to define requirements, document solutions, train users, and drive BI adoption.

The Senior IT Data Engineer will design and implement enterprise data architectures, develop scalable ETL/ELT pipelines, and support data-driven initiatives for the Federal Reserve Board.

Design, build, and maintain scalable ETL/data pipelines and an authoritative Lakehouse using Databricks and cloud platforms. Collaborate on data platform strategy, implement CI/CD, model data, refresh dashboards, apply advanced analytics (LLMs/AI) to unstructured data, and support post-award reporting to improve grant program outcomes.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

Build and maintain scalable data platforms, stores, and ETL/ELT pipelines for structured and unstructured data. Develop software and scripts to retrieve, parse, process, and convert data into usable formats. Support analytics, reporting, monitoring, and platform operations while collaborating with multidisciplinary teams in an agile environment. Work may involve cloud, distributed computing, streaming, NoSQL, and data warehousing technologies.

Designs, builds, and maintains secure cloud data pipelines and centralized repositories; publishes curated datasets; develops WPS AI agents; produces dashboards and visualizations for senior military leadership; supports NATO and annual statutory reporting and prepares technical briefings and deliverables.

Designs and deploys scalable data pipelines, preprocessing workflows, feature-engineering services, and machine learning models for secure DoD environments. The role supports structured and unstructured mission data, exploratory analysis, visualization, model inference APIs, reproducible MLOps workflows, and decision-support capabilities. Responsibilities include collaborating with product, software, platform, DevSecOps, data science, and mission teams; deploying containerized services; participating in Agile delivery; and maintaining technical documentation.

Designs and maintains scalable data pipelines using Databricks, Spark, and cloud services. Builds transformation workflows, integrates APIs, databases, and cloud storage, and supports analytics and reporting. Implements data validation, monitoring, observability, governance, security, and automation. Collaborates with data science, analytics, and business teams to deliver reliable data solutions in AWS and other cloud environments.

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.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Build and maintain scalable data platforms and pipelines that organize structured and unstructured data. Retrieve, merge, analyze, and prepare data from APIs and databases for AI and GenAI models. Collaborate with analysts, engineers, developers, and data consumers in an agile environment while supporting data architecture, ETL, analytics, and machine learning initiatives for mission-driven clients.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

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.