Data Engineer

• Shape the data architecture and data models for a large federal data-lake-plus-warehouse platform on Amazon Web Services (AWS)
• Clean data models and schemas across the lake and the warehouse (Amazon Redshift, S3, and relational databases)
• Design the models and schemas that new and migrated sources land in
• Restructure the existing ecosystem to cut redundancy and protect integrity and accessibility
• Bring new capabilities, tools, and lines of business to the firm

Data Architect

• Build pipelines that move a federal agency's data from source to warehouse on AWS
• Ingest, process, store, and keep data clean and trustworthy at scale
• Create a data lake and manage the design into Redshift with event orchestration and quality validation

• Build and maintain data pipelines using AI tools
• Transform large datasets into clearer, performant datasets
• Collaborate with Product team and Engineers for data-driven customer services
• Ensure data quality and documentation throughout development stages

• Lead a team of 2-3 engineers
• Working with fellow Nava engineers to design, review, and build well-crafted software
• Working in an agile manner to efficiently ship new features that meet user needs
• Design and develop scalable data ingestion and processing pipelines
• Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data
• Implement data validation and quality checks to ensure accuracy and consistency
• Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications
• Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities
• Participate in software design and code reviews
• Maintain security and privacy standards in all aspects of the data pipeline
• Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed

Senior Software Engineer, Data

• Architect and implement enterprise-grade data platforms, including data lakes, warehouses, and real-time pipelines.
• Define and enforce data engineering standards, including ETL/ELT frameworks, data modeling, and quality controls.
• Lead performance optimization of databases, queries, and pipelines (e.g., partitioning, indexing, cost-efficient cloud solutions).
• Design and implement scalable data architecture that ensures high availability, security, and performance.
• Define best practices for data modeling, storage, retrieval, and governance.
• Architect and develop end-to-end ETL/ELT pipelines for structured and unstructured data ingestion, processing, and transformation.

Senior Data Engineer

• Assist TSD with data products by providing highly skilled and authoritative expertise on data engineering methods and best practices, including code-first development approaches and modern pipeline design patterns.
• Design, implement, and maintain an efficient, secure, stable, and flexible data architecture that supports products and end-users, with all assets managed via source control.
• Design, implement, and maintain ELT/ETL pipelines for efficient processing of source data in Azure Synapse and Azure Machine Learning (using SDK V1 and SDK V2)
• Review, maintain, and improve existing architecture and pipelines, including periodic audits to address bottlenecks, deprecated dependencies, and architecture drift.
• Establish quality controls for maintaining all pipelines, and introduce error handling, logging mechanisms, and validation checks.
• Incorporate source control for all pipelines and data analytics codebases to enable iterative code development while ensuring data architecture stability.
• Optimize the ingestion, processing, and storage of a wide variety of datasets and data types, including modern columnar formats such as Parquet.
• Develop self-service capabilities for SBA OIG analysts to query and export data for investigations and audits.
• Coordinate with data scientists to ensure the architecture efficiently supports machine learning algorithms and data pipelines in Azure Machine Learning.
• Develop robust standard operating protocols (SOPs) dictating the authoring, development, validation, publishing, execution, and monitoring of all data pipelines and assets in Azure environment.
• Provide detailed documentation of the data architecture, including data dictionaries, ER diagrams, and pipeline process maps.
• Maintain and expand the environment with additional datasets and services upon request, following a defined intake and testing process prior to production deployment.
• Stay current with emerging AI tools relevant to data engineering and contribute to exploratory efforts evaluating automation and LLM-assisted capabilities.

• Provides architectural leadership and advisory support for mission applications planning, strategy, capability maturity, and target-state definition across the program.
• Helps define prioritization and sequencing for application development and integration by proposing strategic roadmaps that align customer needs with best-in-class technology.
• Supports and oversees technical activities across the contract, mentors technical staff, and drives continuous improvement and innovation into program operations.
• Advises on technology alternatives related to processing, data storage, data access, application development, and enterprise architecture decisions.
• Demonstrates strong expertise in AWS architecture, security in the SDLC, and communication of technical risk and architecture decisions to executive audiences.

Data Architecture Advisor

• Design, build, and maintain robust data pipelines and data lake architecture for both batch and real-time streaming use cases, including high-volume, low-latency data processing
• Improve observability, alerting, and SLOs across data systems so pipelines are easier to monitor and issues are caught early
• Optimize ETL/ELT workflows for performance, scalability, and fault tolerance
• Develop dbt workflows to onboard Evaluation Partners and create end-of-day reporting for partner analysis
• Build and support event-driven architectures and scalable platform components
• Contribute to the orchestration and automation of workflows
• Integrate complex financial APIs and third-party data sources into internal systems
• Collaborate with analytics, product, and ML engineers to develop and deploy reliable data products
• Work on feature pipelines and model-ready data to support ML engineers
• Promote high standards in code quality, testing, and platform reliability
• Participate in Agile ceremonies and foster a collaborative, growth-oriented team culture

• Plan, design, build, test, deploy, and maintain highly scalable web-based services utilizing modern technologies.
• Build, test, and orchestrate data pipelines in Databricks.
• Design and optimize data structures, schemas, and ETL processes.
• Translate business use cases into SQL queries, reports, and dashboards.
• Manage database objects in development environments without impacting production.
• Automate workflows using Databricks Workflows or the Jobs API.
• Integrate code into GitHub/GitLab and support CI/CD practices.
• Diagnose data gaps/quality issues and design test cases for validation.
• Collaborate with developers and analysts to synchronize code and ensure reliability.

• Solving our unique data-lake challenges: transforming and seamlessly normalizing highly varied partner datasets (such as donation data);
• Design robust batch-processing pipelines capable of extracting and loading massive datasets from a variety of internal, external, and public sources;
• Developing processes for data mining, data modeling, and data production;
• Leveraging modern technology knowledge to champion evolving industry trends, updated design patterns, and engineering excellence across the team;
• Autonomously scope, design, and execute complex data projects from day one, turning ambiguous requirements into clear, decisive technical plans;
• Developing robust testing and monitoring systems for scheduled processes;
• Collaborating with cross-functional teams to support their data infrastructure needs;
• Joining each and every one of your colleagues in creating an inclusive workspace.

Staff Data Engineer

• Support and maintain the mission-critical Cost Roll process by ensuring accurate aggregation of healthcare claims data and precise matching of claims to provider demographic data.
• Maintain and monitor data workflows that aggregate healthcare claims data for the Cost Roll process.
• Validate and reconcile claims and provider demographic data to ensure accurate provider matching and data quality.
• Investigate, troubleshoot, and resolve data issues that could impact financial, clinical, and provider management systems.
• Perform data validation, quality checks, and operational support activities to ensure clean and reliable datasets.
• Collaborate with stakeholders to support ongoing data processing, reporting, and operational reliability initiatives.

• Own and manage the Credit Union’s enterprise data platform environment, including Microsoft Fabric workspaces, OneLake architecture, capacity utilization, platform security, governance controls, and overall platform reliability and availability.
• Design, develop, maintain, and optimize data pipelines and ETL/ELT processes to ingest, transform, reconcile, and deliver data from core banking systems, APIs, and external data sources.
• Develop and maintain enterprise data models, shared dimensions, and integrated data structures supporting lending, deposits, general ledger, and other business domains while promoting a trusted single source of truth.
• Support enterprise analytics through the development and maintenance of semantic models, reporting data structures, automation of recurring reporting processes, and enablement of self-service analytics capabilities.
• Implement monitoring, controls, and data quality processes to identify pipeline failures, anomalies, reconciliation issues, and performance concerns; troubleshoot and resolve discrepancies in a timely manner.
• Research, evaluate, and implement opportunities to improve data platform performance, scalability, security, and cost efficiency while contributing to future-state data architecture and roadmap planning.
• Maintain a thorough understanding of and comply with all applicable Credit Union policies, procedures, regulatory requirements, and annual mandatory training, including but not limited to Bank Secrecy Act (BSA), Anti-Money Laundering (AML), OFAC, Cybersecurity, Diversity & Anti-Harassment, and Fair Lending requirements.

Data Platform Administrator

• Lead a distributed engineering team across platform/product engineering, connectors, QA, DevOps/infra, AI implementation, data governance, and support.
• Own delivery, quality, release discipline, and execution of the technical roadmap.
• Install engineering discipline where it's thin: automated testing, QA, CI/CD, release governance, and versioning.
• Drive repeatable, perimeter-safe deployments, including containerization, infrastructure-as-code, secure deployment, and SOC 2 readiness.
• Partner with the Chief Architect on the connector framework, canonical metadata model, architecture decisions, and product IP.
• Build the team: assess current talent, retain the strong, hire the gaps, and align the team plan to the roadmap.
• Push practical AI use across coding, review, testing, ops, and engineering productivity.

Director of Engineering – Data Platform

• Leading the design and development of data infrastructure and systems to facilitate efficient data processing and analysis
• Developing and implementing data pipelines, integration, and transformation solutions to support client needs
• Utilizing advanced technologies and techniques to transform raw data into actionable insights for informed decision-making
• Building and maintaining scalable data architectures using platforms such as Azure Data Factory and Snowflake
• Overseeing the deployment of data engineering solutions, confirming data quality and security
• Collaborating with cross-functional teams to address complex data challenges and drive business growth
• Applying systems thinking to identify underlying problems and opportunities within data processes
• Coaching and mentoring team members to enhance their skills and contribute to high-performing teams
• Validating outcomes with clients and incorporating feedback to refine data solutions
• Directing teams through complex and ambiguous situations, maintaining composure and focus on objectives

Senior Manager – Tax Innovation & Delivery Experience, Data Engineering

• responsible for leading the modernization, optimization, and stabilization of the Wisconsin Medicaid Market's data platform ecosystem across two independent health plan technology stacks.
• owns the market's Data Warehouse and ODS, drives ETL/data movement strategy (including SSIS modernization), and improves the reliability, observability, and security posture of data pipelines supporting critical Medicaid operations.
• partners closely with the Market BI team as their IT counterpart to improve data access and flow, including establishing and managing Databricks pipeline patterns and platform enablement as the BI environment evolves.
• accountable for modernizing and optimizing the market's data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency in a complex dual-stack environment.
• primary IT partner for data platform enablement — improving data access patterns, strengthening pipeline governance, and helping establish a maintainable approach to Databricks pipelines and workflows as the market matures its analytics capabilities.

Lead Data Engineer – Modernization, Reliability

• Help clients find answers in their data to impact important missions
• Develop and deploy the pipelines and platforms that organize and make disparate data meaningful
• Work with a multi-disciplinary team of analysts, data engineers, developers, and data consumers
• Support the assessment, design, developing, and maintenance of scalable platforms

• Evolving data infrastructure and data analytics capabilities
• Building architecturally-sound data systems and pipelines for analytics and data science
• Leading full stack development projects
• Collaborating with cross-functional teams
• Managing data logistics including specifications, transfers, and structures
• Accessing, extracting, and transforming data from various sources using tools like Hadoop, Spark, and SQL
• Supporting analytical proof of concepts and tool exploration
• Developing scalable ETL pipelines and ensuring data quality and reliability
• Assisting in Agile/Lean methodologies and DevOps practices

• Own a Data, AI/ML product portfolio spanning Personalization and Marketing
• Define and drive the technical roadmap, balancing innovation, and agile delivery with operational stability and reliability
• Lead the strategic shift from rule-based campaigns to AI and agent-based orchestration
• Oversee full-scale AI/ML & Data pipelines including deep-learning models and optimization capabilities
• Lead architecture reviews, design forums, and technical sessions across Data Science, Engineering, and Technology teams
• Ensure engineering standards are maintained across the portfolio - covering solution design, security, and Data, AI Ops practices
• Drive agile delivery within the Squad model, managing dependencies across a matrixed environment, and removing blockers
• Coach and grow engineering leaders and senior ICs and build a strong internal talent pipeline
• Serve as a trusted partner to Senior stakeholders across Data Science and Business teams
• Translate complex technical concepts into clear, business-relevant narratives for Executive and non-technical audiences

Lead Director – Data Engineering

• Design and implement scalable data pipelines using AWS services including AWS Glue, Step Functions, Lambda, and Kinesis.
• Build ETL/ELT processes to ingest, transform, and load data from various sources into data warehouses and data lakes.
• Architect and maintain data lakes using S3, implement data cataloging with AWS Glue Catalog, and optimize data storage formats (Parquet, Delta, etc.).
• Design data warehouse solutions using Redshift and integrate with existing RDS/Aurora databases managed by the DBA team.
• Develop real-time and batch data processing solutions using Kinesis Data Streams, Kinesis Analytics, EMR, and AWS Batch.
• Create and maintain data models, schemas, and documentation.
• Work with the DBA team to optimize data access patterns and query performance across relational and analytical databases.
• Build automated data quality checks, monitoring, and alerting systems.
• Implement data governance policies and ensure compliance with data retention, privacy, and security requirements.

AWS Data Engineer

• Own the design of data models and structures for analytics purposes; collaborate with our consulting partner and internal team members to bring them to life and iterate based on what actually works in practice.
• Design data ingestion and transformation pipelines that are maintainable, observable, and appropriately documented—not just functional at launch.
• Define the security model for data access, with genuine attention to the intersection of data privacy requirements, compliance obligations, and the usability needs of internal teams.
• Lead tool evaluation and selection across ingestion, data lake, data governance, and data catalog—with a process that produces clear, reasoned recommendations, not just vendor comparisons.
• Ingest priority data sources into our data warehouse or lake and prepare them for analytical use, including validating that the data is trustworthy before downstream teams depend on it.

Data Engineer jobs in District of Columbia

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Data Architect

Data Engineer

Data Engineer

Senior Software Engineer, Data

Senior Data Engineer

Senior Data Engineer

Data Architecture Advisor

Senior Data Engineer

Senior Data Engineer

Staff Data Engineer

Data Engineer

Data Platform Administrator

Director of Engineering – Data Platform

Senior Manager – Tax Innovation & Delivery Experience, Data Engineering

Lead Data Engineer – Modernization, Reliability

Data Engineer

Data Engineer

Lead Director – Data Engineering

AWS Data Engineer

Data Architect