Data Engineer

• Design and support modern enterprise data and analytics solutions
• Work with large, complex datasets in enterprise environments
• Support modernization of data platforms and architectures
• Develop scalable approaches to data integration, analytics, and management
• Collaborate with cloud, engineering, application, architecture, and program teams
• Support advanced analytics and AI/ML initiatives
• Develop technical documentation, architecture designs, analytical models, and recommendations
• Support development of scalable data platforms and advanced analytical capabilities
• Support a large-scale federal data and technology modernization initiative for the U.S. Census Bureau

Lead Data Scientist / Data Architect

• Provide technical leadership across enterprise data architecture efforts within a large-scale modernization program
• Design and govern scalable data ecosystems including data lakes, lakehouse architectures, data warehouses, marts, and distributed processing platforms
• Define and implement enterprise data models, schemas, standards, retention strategies, and lifecycle management approaches
• Oversee data management, integration, quality, lineage, storage, retention, and governance processes across systems
• Establish metadata management, data catalogs, data dictionaries, and lineage frameworks supporting governance and traceability requirements
• Design and manage large-scale data ingestion, ETL/ELT pipelines, transformation workflows, analytics, and dissemination capabilities
• Support real-time and streaming architectures using event-driven processing and distributed messaging systems
• Design and oversee APIs, system interconnections, interface management processes, and Interface Control Documents (ICDs)
• Support AI/ML-enabled architectures including ML pipelines, MLOps processes, model deployment, and AI governance frameworks such as the NIST AI RMF
• Collaborate with application architects, engineers, data scientists, SMEs, and external vendors to deliver secure, scalable, and high-performing data solutions
• Ensure compliance with federal data management, privacy, and security requirements including NIST, FedRAMP, Zero Trust, ATO processes, encryption, access control, and data sharing standards
• Lead architecture efforts supporting system-of-systems integrations across multiple contractors, vendors, and interdependent platforms
• Implement FinOps and cloud optimization strategies including cost monitoring, tagging, performance tuning, and operational efficiency improvements
• Support operational management of enterprise data platforms including monitoring, maintenance, performance optimization, and lifecycle management (O&M)
• Establish and enforce architecture governance, standards, and best practices across Agile and SAFe delivery teams
• Mentor architects and engineering teams while promoting consistency, governance, and technical excellence

Lead Data Architect

• Build, configure and operate an AI-assisted platform that helps organizations understand, govern and move complex enterprise data
• Translate mission and customer data questions into clear objectives, success measures and evaluation criteria
• Profile, clean and characterize complex multi-source enterprise data
• Identify data gaps, conflicts, quality issues and relationships affecting downstream use
• Build and maintain metadata, data dictionaries and reference vocabularies for customer environments
• Configure and tune AI-assisted capabilities that match, classify, rank and validate data across systems
• Evaluate AI agents and language-model workflows for accuracy, grounding, consistency and failure modes
• Build test sets and track quality over time
• Analyze human review feedback to find systematic errors and improve platform performance
• Support data validation and reconciliation for trusted integrated or migrated data
• Partner with data, software and cloud engineers to move validated improvements into production with version control and measurable results
• Create visualizations, narratives and briefings for technical and non-technical stakeholders
• Handle sensitive data under access controls, governance and auditability
• Maintain reproducible code, experiments and documentation
• Contribute data science expertise to solution planning and proposal efforts as needed

Applied Data Scientist, AI Data Platforms

• Partner with business stakeholders, product owners, architects, and technical teams to understand business objectives, data requirements, and operational needs
• Build data pipelines using multiple data sources, PostgreSQL, AWS Grafana, AWS QuickSight, AWS infrastructure, and associated integration technologies
• Design and implement secure, scalable, resilient, and cost-optimized cloud data solutions
• Define and implement scalable data ingestion, transformation, and consumption patterns
• Establish data engineering standards, best practices, and reusable design patterns
• Design, develop, and optimize conceptual, logical, and physical data models for analytics, reporting, dashboards, and operational systems
• Evaluate emerging technologies and recommend platform and performance improvements
• Ensure data architecture aligns with business requirements, governance standards, and scalability needs
• Collaborate with analytics, reporting, and business intelligence teams to deliver high-quality data products and trusted data assets
• Perform advanced data analysis and profiling using SQL and Python
• Establish monitoring, alerting, observability, and automated recovery mechanisms for critical data pipelines
• Ensure compliance with security, privacy, and regulatory requirements
• Collaborate with Agile teams to deliver iterative business value while maintaining technical quality and delivery commitments
• Communicate technical concepts, risks, solutions, and recommendations to technical and non-technical stakeholders