Data Engineer

• Building state of the art pipelines and data models that are at the heart of the studio decision-making process.
• Working on large-scale lakehouse and warehouse analytics systems that process data feeds in real-time and batch processes.

Lead Data Engineer

• Lead and grow a team of data engineers, providing mentorship and leadership within the function
• Build the future of the data engineering practice to deliver value to internal and external stakeholders while leveraging cutting edge technologies to dramatically accelerate quality, scale, and depth of delivery
• Design and implement scalable data pipelines to collect, process, and store large volumes of critical data from various sources
• Drive the evolution of the Business Data platform by leveraging modern data technologies like Spark, Airflow, and Databricks
• Collaborate across organizational boundaries with Product, Marketing, and Sales to convert business needs into technical requirements
• Authoritatively translate business use cases into well-thought-out data models that evolve with the business

Director, Data Engineering – Business Data

• 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

Senior Data Engineer

• Own data architecture end-to-end. Define how we capture, model, and serve critical business data then implement it in production. You’ll make architectural decisions around storage formats, compute patterns, and SLAs that balance cost, scalability, and consistency.
• Build mission-critical pipelines. Develop and operate streaming and batch data workflows that process high-volume events across multiple domains user activity, transactions, experimentation, marketing performance, and operational telemetry with tight guarantees for latency, completeness, and accuracy.
• Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and real-time applications. Establish and enforce modeling standards, ownership boundaries, and data contracts across teams.
• Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable.
• Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems.
• Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, low-latency data through semantic layers, APIs, and real-time query systems.

Data Engineer, Payments

• Build, lead, mentor, and grow high-performing Operations & Maintenance teams.
• Develop the operational strategy, organizational structure, and execution model supporting large-scale AI infrastructure.
• Establish world-class operational processes, including incident response, change management, problem management, and service reliability.
• Lead major incident management efforts and executive communications during production events.
• Drive operational excellence through proactive monitoring, observability, automation, and continuous improvement initiatives.
• Partner with Engineering to ensure operational readiness for new infrastructure deployments and platform launches.
• Define service level objectives (SLOs), operational KPIs, and reliability metrics across the infrastructure portfolio.
• Build scalable on-call programs, escalation models, runbooks, and operational governance.
• Champion root cause analysis and long-term corrective actions to improve platform resilience.
• Influence infrastructure architecture and operational tooling to improve availability, efficiency, and customer experience.
• Help shape the long-term operations organization as the company expands globally.

Data Center Operations and Maintenance Engineering Leader

• Monitor live data center and GPU infrastructure for performance, capacity, and reliability issues.
• Lead or support incident response for production issues, driving toward fast, effective resolution.
• Collaborate with the engineering teams to make sure monitoring, alerting, and operational tooling are first class and make issues are caught before they impact customers.
• Partner with hardware, networking, orchestration, and infrastructure teams to resolve root causes and prevent recurrence.
• Contribute to runbooks, on-call practices, and operational maturity as the platform scales.

Data Center Operations and Maintenance Engineer

• Design, build, automate, and operate data center networking supporting large-scale GPU clusters, AI workloads, and multi-tenant cloud services.
• Develop secure tenant isolation architectures that ensure performance, scalability, and security across customer environments.
• Design resilient, highly available network architectures across compute, storage, and infrastructure services.
• Work across multiple networking platforms and vendors, including technologies from Arista, Juniper, HPE, and other enterprise networking providers.
• Partner closely with hardware, infrastructure, virtualization, and platform engineering teams to optimize networking for AI training and inference workloads.
• Build infrastructure automation to simplify deployment, configuration management, monitoring, and operational excellence.
• Define networking standards, architecture documentation, and engineering best practices as the infrastructure organization grows.
• Evaluate emerging networking technologies and help shape the long-term evolution of the company's AI networking platform.

Data Center Network Engineer

• Design and manage Feed the Children’s modern cloud data infrastructure, with ownership of Microsoft Fabric and Azure.
• Lead the stand up and evolution of Feed the Children’s Fabric platform and serve as its primary Product Owner.
• Collaborate closely with ERP and CRM teams, and coordinate with Data Governance leadership.
• Ensure seamless integration of data and data flows from various sources, including ERP systems, CRM systems, DHIS2, external datasets and APIs, and other business applications.
• Implement processes and tools to ensure high data quality and consistency across the organization.

Data Architect

• Design, implement, and oversee the enterprise data governance strategy
• Work in close partnership with the Ontology/Semantic Librarian and Data Engineering teams
• Lead the development and enforcement of data governance policies, standards, and best practices
• Collaborate with Data Engineering teams to implement governance in federated querying, GraphQL interfaces, vector databases, and RAG pipelines
• Define and maintain enterprise data stewardship processes
• Establish governance frameworks for data security, privacy, and compliance
• Design and implement data quality, metadata, and lineage strategies
• Contribute to the internal Data Marketplace by defining governance requirements for semantic data products
• Drive data governance training, change management, and adoption across business, engineering, and AI teams
• Monitor and report on governance metrics, risks, and compliance posture for the Data Unification program

Lead Data Engineer – Governance

• Lead the design, development, and governance of enterprise ontologies, semantic layers, and knowledge graphs.
• Combine deep semantic modeling expertise with hands-on implementation of graph technologies, vector databases, and federated data architectures.
• Design and build enterprise ontologies and semantic models that align business objectives with technical implementation.
• Lead the creation and maintenance of semantic layer graphs, knowledge graphs, and entity graphs.
• Develop and implement strategies for using vector databases and graph databases to enable powerful LLM-augmented search and reasoning.
• Partner with data engineering, AI, and business teams to translate business goals into ontological models.
• Contribute to the development of a unified data access layer that supports querying across diverse data sources.
• Implement and enforce enterprise security models through the ontology and semantic layer.
• Collaborate on AI integration initiatives, including building ontology-driven agents.
• Establish ontology governance processes and contribute to the internal Data Marketplace.

Lead Data and Ontology Engineer

• Architect and optimize large-scale distributed pipelines
• Scale up inference throughput across the pipeline
• Design systems that reliably store, index, and serve billions of data points
• Apply deep expertise in distributed systems and frameworks
• Partner with modeling teams to understand data that improves training outcomes
• Operate as a hands-on technical leader

Principal Scientist – Data Pipeline Engineer

• Designing and implementing AI systems to transform raw data into actionable insights
• Developing scalable machine learning models to support client decision-making processes
• Collaborating with cross-functional teams to integrate AI solutions into existing platforms
• Conducting data wrangling and preprocessing to prepare datasets for analysis
• Applying advanced algorithms to enhance the performance of AI models
• Building and deploying software systems that leverage AI and machine learning technologies
• Analyzing complex data sets to identify patterns and trends that inform business strategies
• Supporting client engagements by delivering quality work and adapting to diverse project requirements
• Engaging in continuous learning to deepen technical skills and knowledge in AI engineering
• Upholding professional and technical standards while contributing to team success

AI Engineer, AI Experienced Associate

• 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

• 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

• Size, architect, and design data protection, disaster recovery, and scale-out storage solutions
• Bring new products and processes to life to meet customer demand
• Serve as the escalation path for L1, L2, and L3 support engineers
• Work directly with customers to solve challenging design and support issues
• Automate the collection of backup system health, backup and recovery jobs, and general environment status
• Monitor backup systems and performance, identify issues needing attention

Data Protection Engineer

• 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

• Own and optimize scheduled workflows, including dependency management, resource controls, and alerting (AutoSys used heavily).
• Participate in an on-call rotation and respond to production pipeline failures; troubleshoot, restore service, and drive root cause fixes to prevent recurrence.
• Build and maintain validation checks and reconciliation controls to ensure data integrity across critical datasets.
• Support SOX (Sarbanes-Oxley) readiness by designing, documenting, and evidencing controls over financial and billing data pipelines, and partnering with internal audit and the third-party assessor on testing and remediation.
• Maintain and enhance existing production data pipelines for ingestion, transformation, and storage of large datasets; implement pragmatic modernization where it reduces operational risk.
• Troubleshoot and resolve data pipeline and ETL failures, implementing robust monitoring and alerting systems.
• Automate manual run steps and harden workflows to reduce paging/noise and improve recovery time.
• Optimize data models for analytics and business intelligence reporting.
• Build and maintain data infrastructure, ensuring performance, reliability, and scalability.
• Implement best practices for data governance, security, and compliance.
• Work with structured and unstructured data, integrating data from various sources including databases, APIs, and streaming platforms.
• Collaborate with data analysts, data scientists, and business stakeholders to understand data needs and design appropriate solutions.
• Mentor and train junior engineers, fostering a culture of learning and innovation.
• Develop and maintain documentation for data engineering processes and workflows.

• Design, craft and own high quality foundational datasets to produce reliable, meaningful inputs for machine learning models and subscriber experiences
• Source, analyze and validate diverse upstream data sources from upstream partner teams and raw signals using exploratory data analysis
• Identify and build new user and content data features for recommendation systems through collaboration with machine learning engineers
• Enhance existing feature quality through experimentation, analysis and iterative development
• Build, optimize and maintain large scale data processing jobs, pipelines and workloads
• Improve data quality and reliability through implementing checks, anomaly detection, testing, monitoring and alerting
• Contribute to operational excellence by following observability best practices and participating in an on-call rotation to support critical production data pipelines

Data Engineer II

• 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.

Data Engineer jobs in Washington

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Lead Data Engineer

Director, Data Engineering – Business Data

Senior Data Engineer

Data Engineer, Payments

Data Center Operations and Maintenance Engineering Leader

Data Center Operations and Maintenance Engineer

Data Center Network Engineer

Data Architect

Lead Data Engineer – Governance

Lead Data and Ontology Engineer

Principal Scientist – Data Pipeline Engineer

AI Engineer, AI Experienced Associate

Data Architect

Data Engineer

Data Protection Engineer

Data Engineer

Senior Software Engineer, Data

Senior Data Engineer

Data Engineer II

Senior Data Engineer