• Design and implement enterprise data architectures including Data Lakes, Data Warehouses, Data Fabric, and MDM solutions. • Build scalable data pipelines and data integration frameworks. • Define data governance, quality, lineage, and security standards. • Enable AI/ML initiatives through robust data engineering and architecture practices. • Collaborate with business and technology teams to develop data strategies and roadmaps. • Optimize data platforms for performance, scalability, and reliability.

• Participation and representation in a client facing Data Governance Committee, including making recommendations, guiding decisions and recommendations within the scope of Client, State and Federal data governance policies, practices, and standards relevant to data management • Ensure and maintain to the highest standards the quality and security of data within program engagement through adherence to processes and procedures for access controls and monitoring of data activities and use to protect data integrity, including ensuring compliance for the preservation and protection of Protected Health Information (PHI) and Protected Identification Information (PII) • Collaborate with stakeholders in the design, testing, implementation and ongoing maintenance and support activities of data services • Assist the Client in solving data-related issues by managing data corruption or mapping data between program areas • Maintain internally established data quality reporting metrics, evaluate, and identify issues/corrections and coordinate and implement data management best practices • Identify data assets, lineage, and business rules within data domains to ensure data element continuity and avoid data conflict • Manage data design, creation and management of database architecture and data repositories • Maintain accurate and current content within the data management plan including all diagrams, workflows, and all other data related documentation • Conduct and comply with data reviews and auditing to ensure adherence to current standards, policies and design of the database architecture and repositories • Ensure documentation and processes are followed, including Architectural Board approvals and other necessary approvals, for all data architecture changes

• Design and operate the cloud data infrastructure powering AI initiatives. • Architect production-scale data lakes on AWS. • Build real-time ingestion and observability pipelines. • Own the vector search and embedding layers that feed RAG systems and autonomous agents.

Role Description Lead the architectural vision for a next-generation data layer designed specifically for Agentic AI. You will define high-performance schemas, orchestrate complex hybrid-database environments (Snowflake/Kinetica/NoSQL), and serve as the primary technical liaison for our customers. This is a high-visibility role blending deep technical governance with strategic relationship development. Key Responsibilities - System Architecture: Design the end-to-end blueprint for a modern data layer that seamlessly integrates structured, unstructured, and relational (Graph) data for AI agents. - Schema & Ontology Design: Define multi-tenant schemas and Knowledge Graph ontologies that allow LLM agents to perform complex reasoning and cross-domain data retrieval. - Database Administration & Governance: Oversee the health, security, and performance optimization of our data clusters (Snowflake/Kinetica), ensuring 99.9% availability for mission-critical AI workflows. - Strategic Client Fronting: Act as the "Face of Engineering" for the customer. Lead discovery workshops, manage technical expectations, and align the architectural roadmap with their business objectives. - Performance Engineering: Establish benchmarks for data latency and retrieval accuracy, ensuring the data layer can keep pace with the real-time demands of agentic execution. Qualifications - Proven expertise in architecting for Snowflake (Data Cloud) and Kinetica (Real-time/Vector/OLAP). - Ability to design Property Graphs or RDF schemas that map enterprise entities into a machine-readable "World Model." - Deep knowledge of data orchestration patterns (Change Data Capture, Streaming, and Batch) to ensure data freshness. - Strong DBA skills—partitioning strategies, indexing, vacuuming, and resource scaling in cloud-native environments. Good to Have (The “Agentic” Edge) - Experience with tools like Cube or dbt Semantic Layer to provide a consistent "Language" for AI agents to query. - Knowledge of RBAC and Row-Level Security (RLS) within an AI context—ensuring agents only "see" what they are authorized to access. - Experience designing API-first data layers that agents can use as "Tools" (e.g., function calling). Benefits - Make an impact at one of the world’s fastest-growing AI-first digital engineering companies. - Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues. - Work where innovation happens - work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines. - Stay ahead of the curve—immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies. - If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!