Agentic Data Architect

• POSITION OVERVIEW: The Agentic Data Architect is the authority on how data flows into, through, and out of agentic
• systems. This role ensures that agents have access to the right knowledge, at the right fidelity, with appropriate
• governance — spanning retrieval-augmented generation (RAG), knowledge graphs, structured data access, and agent
• memory architecture.

RESPONSIBILITIES

• Design end-to-end data architectures that support agentic workloads: ingestion pipelines, chunking strategies, embedding models, vector stores, and retrieval layers.
• Define patterns for agent memory: short-term working memory, episodic memory, semantic memory, and procedural memory stores.
• Architect grounding strategies for agents operating over enterprise data sources (data lakes, warehouses, APIs, documents, real-time streams).
• Establish data quality, freshness, and lineage standards for knowledge assets consumed by agents.
• Lead the selection and integration of vector databases, graph databases, and hybrid search systems.
• Partner with Data Governance and Legal to implement access controls, PII redaction, and audit trails within agent data pipelines.
• Define evaluation frameworks for retrieval quality, hallucination rates, and knowledge currency.
• Collaborate with the Agentic Platform Architect to integrate data services into the agent platform.
• Drive spec-driven development for all data architecture work — producing data contracts, schema definitions,

pipeline design docs, and retrieval system specs before implementation begins.

• Engage directly with business, data, and product stakeholders to understand knowledge requirements, data ownership boundaries, and governance constraints, translating them into concrete data architecture decisions.

QUALIFICATIONS (KNOWLEDGE, SKILLS, AND EXPERIENCE)

• 8+ years of data architecture, data engineering, or ML engineering experience; 2+ years with AI/ML or LLM based systems.
• Deep expertise in modern data stack technologies: data lakes and lakehouses, cloud data warehouses, and streaming data platforms.
• Strong knowledge of traditional dimensional data architecture and modeling for analytical systems.
• Strong command of SQL and NoSQL data systems, including design trade-offs across relational and non relational patterns.
• Hands-on experience designing and scaling RAG pipelines (chunking, embedding, indexing, hybrid search, reranking).
• Strong understanding of vector and graph database technologies and their trade-offs for agentic retrieval use cases.
• Experience with data governance frameworks and enterprise data security.
• Demonstrated practice of spec-driven development: producing data contracts, schema docs, and pipeline design specs that precede implementation.
• Proven ability to engage directly with business, legal, and data stakeholders to align on knowledge requirements, data ownership, and governance constraints.

Preferred Qualifications

• Experience with knowledge graph construction and graph-based retrieval patterns.
• Background in NLP, information retrieval, or search engineering.
• Familiarity with structured data agents and natural language interfaces over relational and semantic data layers.

PHYSICAL REQUIREMENTS

• Repetitive movement of hands and fingers-typing and/or writing
• Have close visual acuity to perform an activity such as: viewing a computer terminal, extensive reading
• Occasional standing and walking

• The duties and responsibilities described are not a comprehensive list. Additional tasks may be assigned to the employee
• from time to time, and the scope of the job may change as necessitated by business demands.
• Compensation Range: $120,000 - $160,000 annually