Agentic Data Architect

ProSearch

21h•$120,000 - $160,000

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.