Data Engineer

• Provide technical leadership to data engineers, setting standards for solution design, coding practices, data governance, and quality.
• Define and evolve the enterprise data architecture leveraging Snowflake, Data Vault 2.0, and modern event driven and ELT frameworks.
• Lead architecture reviews, establish engineering best practices, and guide platform modernization efforts.
• Mentor engineering team members, facilitate code reviews, and promote continuous learning and innovation.
• Architect and oversee the delivery of scalable data pipelines, ingestion frameworks, and transformation processes using Snowflake, Python, Spark, Informatica (PowerCenter/IDMC), AWS Glue, and cloud-native tooling.
• Design and maintain enterprise data models, including Data Vault 2.0 (Hubs, Links, Satellites, PIT, Bridge structures) and dimensional models.
• Develop and optimize Snowflake platform capabilities, including: Snowpipe, Streams, Tasks, File Formats, External Stages Dynamic Tables and ELT pipeline automation Warehouse sizing, performance tuning, and cost optimization.
• Implement scalable batch, micro-batch, and real-time ingestion solutions following best practices.
• Translate complex business requirements into technical design specifications and actionable development plans.
• Partner with product owners, business stakeholders, architects, and analytics teams to deliver high-impact data solutions.
• Manage technical execution across multiple initiatives, ensuring alignment with enterprise priorities, data strategies, and delivery timelines.
• Oversee documentation, deployment readiness, testing processes, and quality assurance for production releases.
• Ensure operational reliability, data accuracy, and performance of enterprise data warehouse and analytical environments.
• Lead root-cause analysis for production issues and drive implementation of long-term preventative solutions. Implement robust monitoring frameworks using tools such as Snowflake Resource Monitors, CloudWatch, Datadog, or equivalent observability platforms.
• Conduct performance optimization on Snowflake workloads, SQL queries, pipelines, and integrations.

Senior Data Engineer

• Leads the architecture, engineering, and ongoing operation of the security data lake and associated data products supporting multiple security technologies and data sources
• Establishes the technical roadmap, engineering standards, reusable design patterns, and platform operating model
• Acts as a subject matter expert among peers, with manager and senior management on all matters pertaining to data platforms and the supporting architecture
• Develop and maintain the end-to-end architecture of the security data lake, including ingestion, storage, transformation, enrichment, semantic, and consumption layers
• Ensure data pipelines comply with security policies and regulatory requirements
• Develop testing plans to ensure quality of implementation
• Provide trouble resolution on complex problems and lead implementations for data pipelines, Microsoft Fabric, Azure DevOps, REST API, and source-system integration issues
• Maintain relationship with key vendors
• Create presentations and seek IT management approval and acceptance of significant replacements or reconfigurations of the data pipeline

• Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies
• Work with a team of developers with deep experience in machine learning, distributed microservices, and full stack systems
• Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake
• Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, and mentoring other members of the engineering community
• Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
• Perform unit tests and conduct reviews with other team members to make sure your code is rigorously designed, elegantly coded, and effectively tuned for performance.

Lead Data Engineer

• Design and implement scalable data architectures supporting enterprise applications.
• Develop and maintain data ingestion, transformation, and processing pipelines.
• Engineer secure cloud-native data solutions within an IL5 environment.
• Develop backend services and APIs that support modern data platforms.
• Support data science initiatives through preparation, transformation, and validation of datasets.
• Generate synthetic, representative datasets using Government-provided schemas and statistical profiles when production data is unavailable.
• Collaborate with QA engineers to validate data quality, functionality, and system performance.
• Participate in software design, code reviews, testing, and technical documentation.
• Work directly with Government stakeholders and technical teams to gather requirements and deliver mission-focused solutions.
• Ensure compliance with security requirements, coding standards, and Agile development best practices.

Data Engineer / Data Architect, Mid to Senior – Top Secret Clearance Required

• Design, build, and maintain scalable data pipelines across internal databases, vendor systems, and our data warehouse
• Support both batch and event-driven or CDC-based data workflows
• Improve pipeline reliability, observability, and performance
• Troubleshoot issues in production and proactively solve data quality or system problems
• Build and maintain data warehouse schemas and transformation layers to support analytics and operational use cases
• Communicate clearly about implementation choices, tradeoffs, and technical constraints
• Identify opportunities to improve data workflows through better tooling, automation, and engineering practices
• Help shape scalable patterns for how data is ingested, transformed, stored, and accessed

• You will own and evolve our data pipelines on GCP — building new ones, hardening existing ones, improving data quality, and making clean, trustworthy data available across the organisation.
• Designing ELT/ETL processes on BigQuery and ClickHouse, building real-time pipelines on Pub/Sub and Kafka with Dataflow (and where it fits, Flink/Spark).
• Orchestrating workflows with Airflow, and ensuring data is properly cleaned, modelled, and served for analytics, ML training, and online inference.
• Partnering with ML engineers on feature pipelines, monitoring data drift, and keeping models well-fed and retrained as needed.
• Consuming and building REST APIs, integrate with third-party SaaS sources, and treating infrastructure as code.
• Collaborating closely with Infrastructure, Software Engineering, Product, and ML/AI engineers.
• Helping drive the migration off Snowflake onto GCP-native stack — and retire shadow pipelines along the way.
• Owning the orchestration layer in Airflow, including SLAs, retries, and data quality gates.
• Modeling data for analytics and for ML — including feature pipelines that serve both training and low-latency online inference.
• Capturing requirements from stakeholders and translating them into pragmatic, well-scoped data products.
• Continuously improving data quality, reliability, observability, and cost efficiency.
• Identifying new data sources worth acquiring and integrating them cleanly.

• Support Army logistics enterprise data management through data governance, data quality, metadata management, data modeling, and strategic initiatives.
• Conduct research and analysis to evaluate enterprise data, business processes, data quality, and emerging issues affecting Army logistics operations.
• Develop and maintain enterprise data models, business rules, metadata, and documentation supporting Army logistics information requirements.
• Support data governance activities by implementing standards, policies, procedures, and best practices that improve enterprise data management.
• Analyze, validate, and monitor data quality to identify inconsistencies, recommend corrective actions, and improve data integrity across enterprise systems.
• Collect, consolidate, validate, and analyze information from multiple enterprise data sources to support executive decision making and organizational priorities.
• Develop executive-ready briefings, reports, decision papers, presentations, dashboards, and analytical products supporting Army logistics leadership.
• Coordinate activities and recommendations across multiple Army organizations and stakeholders to improve enterprise data management and governance.
• Facilitate meetings, working groups, and collaborative planning sessions while documenting decisions and tracking follow-up actions.
• Support enterprise data stewardship, information management, reporting, and modernization initiatives that improve organizational performance.
• Develop performance metrics, dashboards, and analytical products to support leadership visibility and informed decision making.
• Support business development activities, including market research, proposal support, solution development, and client engagement, as required.
• Perform additional consulting, analytical, and project support activities as assigned.

• 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

• Develop and implement object-oriented code and scripting languages to support the interface and accounting systems
• Implement solutions that enhance, streamline and automate the management of our IT systems
• Assist with the configuration, implementation and maintenance of the technical environments for the current development, staging and production environments
• Code and maintain system interfaces and extracts to handle the migration accounting information to ancillary systems
• Develop technical and functional test plans and scripts to validate the successful design, development, and implementation of system and reporting deliverables
• Code and support multiple in-place programs (Coordinate with project, support, and business analysts to develop functional and technical requirements, as well as translate detailed specifications into new or enhanced software solutions.)
• Assist with performance management and load/stress testing for the interface and accounting systems, as well as create interfaces and load-scripts to ancillary financial and reporting systems
• Manage the day-to-day operation and coordination of interface and accounting systems processes and enhancements
• Monitor process metrics and lead process improvement efforts
• Manage processing and enhancements for accounting feeds
• Support the Genworth Investments functions by troubleshooting and identify root cause analysis of application problems. Identify action items and make recommendations to address process and coding issues.
• Create and maintain derivatives support knowledge base that includes technical and process documentation.
• Some travel may be required (<15%) between Genworth's Richmond and Stamford locations

• Support the development and delivery of advanced data analytics and visual analytics capabilities for mission-critical environments.
• Work closely with software engineers, data scientists, architects, and mission stakeholders to develop innovative solutions that support data-driven decision-making.

Data Engineer – Mid-Level

• Design and implement the canonical data model for Vultr’s control plane ERP transformation: product catalog, chart of accounts, financial transactions, cost centers
• Build and maintain the internal APIs and event streams that Finance, Analytics, and downstream systems consume
• Refactor existing administer code toward clean domain boundaries (no more conflated orders/ledger/report tables)
• Implement data upgrade/migration paths from legacy schemas without breaking production
• Write the integration layer between administer and Vultr's existing billing, provisioning, and reporting systems
• Establish data validation and integrity checks across the financial data layer
• Collaborate with the Product Manager to translate finance domain requirements into durable schema designs
• Document architectural decisions and data model contracts for downstream consumers

Senior Software Engineer, ERP, Data & Domain

• Responsible for maintaining an existing legacy catalog of data related to debit card authorization, settlement and servicing
• Assist teams in consuming data for customer facing and back office use cases
• Elevate data to the next level, making it easier to consume debit card data
• Automate multiple use cases and build for the future with new data input patterns
• Become a subject matter expert and work with high performers

Senior Associate, Debit Platform Data Product Manager

• Expertise and fluency in AI/ML techniques.
• Design, build, and maintain Extract, Transform, and Load (ETL) pipelines using industry best practices.
• Work with data management teams to design transform scripts in languages like Python to convert raw data to standardized data schemas.
• Implement and manage containerization strategies using OpenShift/Kubernetes.
• Collaborate with development teams to integrate CI/CD pipelines into the software development lifecycle.
• Monitor and optimize system performance, reliability, and availability.
• Automate infrastructure and application deployment processes.
• Ensure security best practices are implemented throughout all DevOps procedures.
• Troubleshoot and resolve issues in development, test, and production environments.

AI/ML Engineer, Data Scientist

• Develop and deliver advanced data analytics and visual analytics capabilities for mission-critical environments.
• Work closely with software engineers, data scientists, architects, and mission stakeholders.
• Develop innovative solutions that support data-driven decision-making.

• Build and maintain the data models and transformations that power reporting in the Skimmer product, leveraging tools like Fivetran transformations, dbt, and Sigma Computing materializations
• Build and maintain customer-facing data models and reports in Sigma Computing, embedded within the Skimmer application
• Manage and extend ingestion using Fivetran across a growing set of sources
• Develop and maintain data transformations in Python and SQL against our Snowflake data warehouse
• Partner with product and engineering to turn customer needs into reliable, performant reporting experiences
• Collaborate with application engineers to understand source systems and ensure clean, reliable data capture
• Monitor data quality, reliability, and report performance, and troubleshoot issues as they arise
• Document data models, definitions, and architecture to support a growing team

• Define the enterprise data architecture supporting operational systems, analytical platforms, and AI initiatives.
• Establish the event-driven integration patterns, governance and metadata standards, and reusable data products.
• Assess legacy systems to understand data structures, data quality, storage mechanisms, and transformation logic.
• Work closely with the Systems Architect on enterprise direction, with Solutions Architects on product-level data needs, and with Data Engineers.
• Define the target-state data architecture spanning operational stores and the analytical data lake.
• Develop conceptual, logical, and physical data models that support both transactional systems and analytics.
• Design event-driven integration patterns that feed the EDL, using messaging and an enterprise service bus.
• Define reusable data products with clear ownership, documented schemas, lineage, quality expectations, and access policies.
• Define data governance, metadata, and cataloging standards.
• Ensure the data architecture supports analytics, machine learning, and Generative AI use cases.

Data Architect, Advisor

• Own the building, maintenance, and optimization of pipelines to ingest data from both operational databases and third-party tools into a data lake/warehouse.
• Architect highly efficient ingestion patterns that handle evolving data schemas and high-volume, multi-source data streams seamlessly.
• Optimize pipeline performance to ensure maximum uptime, high throughput, and cost-effective compute usage.
• Use dbt to transform raw data within the data warehouse into structured, production-ready schemas.
• Write templated SQL and Jinja code to enforce macro-driven, modular, and DRY (Don't Repeat Yourself) development practices.
• Perform rigorous data quality checks by implementing native dbt tests.
• Set up monitoring and alerting frameworks around both ingestion routines and dbt builds.
• Track pipeline health metrics to measure and report on overall data freshness and platform reliability.
• Build data applications that interact with the data warehouse to empower decentralized self-service analytics.
• Build internal tooling and libraries to facilitate analytics and ML work.

Staff Data Engineer

• Implement data engineering activities on mission-driven projects
• Deploy and develop pipelines and platforms that organize and make data meaningful
• Work with and guide a multi-disciplinary team of analysts, data engineers, developers, and data consumers
• Manage the assessment, design, building, and maintenance of scalable platforms

Data Engineer – Mid

• Design, build, and maintain robust, scalable batch and streaming data processing, storage, and integration pipelines using technologies such as Apache Airflow or Benthos.
• Interpret requirements and design specifications, taking full ownership of building features from the ground up. Mentor and lead the rest of your team to deliver the right solutions.
• Write clean, well-documented, scalable, extensible, and testable code to ensure application quality and maintainability.
• Partner with data scientists and engineers to create semantic data models representing complex vehicular systems, and integrate applications cleanly across other Shift5 componentry.
• Build scalable data products for data scientists, transportation engineers, and executives to drive insights and decision-making.
• Create efficient, reliable, cost-effective, dynamically scalable, and observable solutions utilizing AWS cloud services.
• Analyze complex data sets to create data ontologies, verify data quality/integrity, and ensure data accuracy throughout pipelines.
• Support the design process and occasionally travel to customer sites (estimated a few times per year) to collaborate with Field Engineers on data integration and deployment.

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Data Engineer jobs in Virginia

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Senior Data Engineer

Senior Data Engineer

Lead Data Engineer

Data Engineer / Data Architect, Mid to Senior – Top Secret Clearance Required

Senior Data Engineer

Senior Data Engineer

Data Engineer

Senior Software Engineer, Data

Data Engineer

Data Engineer – Mid-Level

Senior Software Engineer, ERP, Data & Domain

Senior Associate, Debit Platform Data Product Manager

AI/ML Engineer, Data Scientist

Senior Data Engineer

Data Engineer

Data Architect, Advisor

Staff Data Engineer

Data Engineer – Mid

Staff Data Engineer

Vice President, Product Data Platform – Economic Intelligence