Data Engineer

• Lead the design, development, and implementation of scalable cloud-based data solutions
• Provide technical oversight and code review for engineering teams to ensure quality, scalability, and maintainability
• Partner with business stakeholders, product owners, architects, and engineering teams to translate business needs into technical solutions
• Guide technical decision-making related to data ingestion, transformation, storage, and integration
• Support solutions involving geospatial, imagery, and enterprise data platforms
• Collaborate with cross-functional teams to evaluate tools, technologies, and implementation strategies
• Mentor engineers and establish engineering best practices across projects
• Communicate technical concepts effectively to technical and non-technical audiences
• Participate in solution discovery, planning, estimation, and delivery activities

Lead Geospatial Data Engineer

• Deliver client value and ensure high client satisfaction
• Design, enhance, and maintain production-grade data pipelines supporting model output aggregation and downstream risk analysis
• Establish and mature repository governance practices, including branching strategies, pull request standards, merge policies, release tagging, and version control workflows
• Own release engineering practices for reproducible, traceable, and auditable production releases
• Refactor and improve pipeline code for modularity, maintainability, scalability, and documentation quality
• Develop configuration-driven pipeline patterns across environments and releases
• Support testing, validation, benchmarking, and change management for critical data pipelines
• Partner with data scientists, machine learning engineers, data engineers, product stakeholders, and other technical teams
• Align teams on schemas, interfaces, inputs, and delivery expectations
• Translate complex technical concepts into clear updates for technical and non-technical stakeholders
• Improve structure and governance in codebases, repositories, and engineering workflows
• Contribute to engineering best practices in a regulated, audit-sensitive delivery environment

Lead Data Engineer – PySpark, Palantir Foundry

• Deliver high-quality modern data solutions
• Collaborate with engineering, analyst, data scientist, and product teams in an agile environment
• Design, integrate, and implement on-premises data solutions
• Support migrations of existing workloads to the cloud
• Design and develop data solutions using modern programming languages
• Process payer files into the data warehouse
• Support and implement cloud-based and on-premises data warehousing services

Senior Data Engineer

• Lead the design, development, testing, and implementation of enterprise data ingestion and transformation pipelines
• Consolidate investigative records, hotline data, DHS datasets, and federal/commercial data sources into a centralized, governed data platform
• Oversee migration from legacy Informatica platforms and on-premises Python scripts to modern cloud-based data integration solutions
• Architect scalable batch, incremental, streaming, and metadata-driven data integration pipelines
• Lead operation, automation, monitoring, reporting, performance optimization, failure remediation, data quality validation, and SLA compliance for production pipelines
• Design and implement integrations with DHS data fabric, Delta Sharing, APIs, secure file transfer mechanisms, and data-sharing protocols
• Coordinate phased data onboarding, including technical discovery, source system analysis, data profiling, pipeline design, testing, and production deployment
• Provide technical leadership for data architecture and cloud analytics engineering, including lakehouse design and relational, graph, and geospatial data modeling
• Lead and coordinate data engineering resources, including surge support for complex analytical needs and forensic data reconstruction
• Develop and maintain technical documentation, data lineage, pipeline architecture, integration specifications, operational runbooks, and best practices
• Collaborate with Government stakeholders, platform architects, AI engineers, and governance specialists to support mission analytics, data governance, and AI model consumption

Chief Data Engineer

• Lead the design, development, and implementation of scalable cloud-based data solutions
• Provide technical oversight and code review for engineering teams to ensure quality, scalability, and maintainability
• Partner with business stakeholders, product owners, architects, and engineering teams to translate business needs into technical solutions
• Guide technical decision-making related to data ingestion, transformation, storage, and integration
• Support solutions involving geospatial, imagery, and enterprise data platforms
• Collaborate with cross-functional teams to evaluate tools, technologies, and implementation strategies
• Mentor engineers and establish engineering best practices across projects
• Communicate technical concepts to technical and non-technical audiences
• Participate in solution discovery, planning, estimation, and delivery activities

• Design, build, and operate highly available data and AI infrastructure on Kubernetes and public cloud platforms
• Develop scalable platforms for streaming, batch processing, and real-time data workloads using Kafka, Spark, and Flink
• Build and evolve AI infrastructure, including AI gateways, model-routing layers, traffic management, rate limiting, authentication, observability, and usage controls
• Develop self-service capabilities enabling data, AI, and application teams to deploy and operate workloads safely and independently
• Partner with application, data, machine learning, security, and infrastructure teams to translate emerging requirements into durable platform capabilities
• Improve the scalability, reliability, and efficiency of data and AI infrastructure
• Reduce operational effort for deploying and managing data or AI workloads
• Improve visibility into system performance, reliability, capacity, and cost
• Establish reusable platform capabilities adopted by engineering teams
• Help define the technical direction for next-generation data and AI infrastructure

Senior Software Engineer, Data and AI Infrastructure

• Architect and implement key components of Docker’s data platform and drive technical direction
• Design and build scalable data infrastructure using Snowflake, AWS, Airflow, DBT, and Sigma
• Build end-to-end data pipelines for real-time and batch analytics across Docker’s product ecosystem
• Evaluate data platform technologies, architectural patterns, and engineering best practices
• Establish standards for data quality, testing, monitoring, and operational excellence
• Build high-throughput data systems supporting high-volume user interactions
• Develop DBT data transformations and models for analytics and business intelligence
• Develop and maintain Apache Airflow orchestration workflows
• Optimize Snowflake performance and cost efficiency
• Build data APIs and services for self-service analytics and downstream integrations
• Translate business and product analytics requirements into technical solutions
• Collaborate with Data Scientists and Analysts on analytics, ML, and BI capabilities
• Deliver accurate reporting and operational dashboards with Finance, Sales, and Marketing
• Support customer-facing analytics and embedded reporting
• Partner with Security and Compliance on data governance and regulatory compliance
• Ensure reliability, monitoring, alerting, and incident response for owned components
• Implement data quality checks and automated pipeline and transformation testing
• Establish disaster recovery and business continuity procedures
• Troubleshoot and resolve complex issues affecting data availability and accuracy
• Mentor engineers on system design, technical execution, and data engineering practices
• Conduct technical design reviews and provide architecture feedback
• Share knowledge through documentation, tech talks, and cross-team collaboration
• Participate in hiring and technical assessments for data engineering roles

Data Engineer jobs in Washington

Lead Geospatial Data Engineer

Lead Data Engineer – PySpark, Palantir Foundry

Senior Data Engineer

Chief Data Engineer

Lead Geospatial Data Engineer

Senior Software Engineer, Data and AI Infrastructure

Senior Software Engineer, Data Infrastructure