Senior AI Data & Knowledge Architect Lead

Booz Allen Hamilton•McLean, VA

4d•$142,900 - $266,000•Hybrid

About The Position

Design enterprise data architectures and build pipelines, knowledge indexes, and retrieval services that ground AI agents in authorized enterprise information. Work with source-system owners and engineering teams to define data contracts, integrate structured and unstructured sources, and maintain permissions, lineage, quality, and freshness. Own the reliability of data and retrieval services while partnering with AI Engineering on application retrieval behavior and response quality. U.S. citizenship is required due to the nature of work performed within the facility.

Requirements

10+ years of experience designing or implementing enterprise data platforms, lakehouse architectures, and integrations for structured and unstructured data.
Experience defining data models, schemas, data contracts, APIs, and access patterns that support downstream applications.
Experience with data governance, source permissions, lineage, quality controls, and monitoring for freshness and availability.
Experience developing, testing, and operating ETL or ELT pipelines, including proficiency in SQL and Python.
Knowledge of retrieval-augmented generation, embeddings, vector indexes, and semantic or hybrid search.
Ability to troubleshoot ingestion and retrieval failures and assess relevance, latency, scalability, and data quality.
Ability to translate enterprise requirements into architecture decisions, integration standards, and governance patterns.
Bachelor's degree.
U.S. citizenship is required.

Nice To Haves

Experience with Databricks, Spark, Delta Lake, vector databases, or comparable platforms.
Experience with permission-aware retrieval, metadata enrichment, incremental ingestion, or knowledge engineering.
Experience delivering reusable customer connectors and data services for production AI applications.

Responsibilities

Design enterprise data architectures for AI applications.
Build pipelines, knowledge indexes, and retrieval services grounded in authorized enterprise information.
Work with source-system owners and engineering teams to define data contracts and integrate structured and unstructured sources.
Maintain permissions, lineage, quality, and freshness of data.
Own the reliability of data and retrieval services.
Partner with AI Engineering on application retrieval behavior and response quality.
Troubleshoot ingestion and retrieval failures and assess relevance, latency, scalability, and data quality.
Translate enterprise requirements into architecture decisions, integration standards, and governance patterns.