below marketmost similar roles pay hereabove market
This role pays more than
53%
of similar roles. Most pay
$162,000–$239,662
— the blue band above.
At the midpoint, this role pays about
$204k
versus about
$201k
for comparable roles.
Based on 240 similar postings.
Employer
About Booz Allen Hamilton
Booz Allen Hamilton is a management and technology consulting firm that provides analytics, digital, engineering, and cybersecurity solutions primarily to U.S. government agencies and commercial clients. Industry: Management & Technology Consulting
Booz Allen Hamilton currently has
1660 open roles
on FindRole.
Listed pay typically runs
$86,800–$198,000
across 908 roles with salary data.
The AI Data & Knowledge Architect, Senior Manager designs data architectures and builds pipelines, knowledge indexes, and retrieval services to ground AI agents in authorized enterprise information. Working with source-system owners and engineering teams, you will define data contracts, integrate structured and unstructured sources, and maintain permissions, lineage, quality, and freshness. You will own the reliability of retrieval services while partnering with AI engineering on application behavior and response quality. Required skills include experience with Databricks, Spark, Delta Lake, or vector databases, as well as SQL and Python for developing ETL or ELT pipelines. You must understand retrieval augmented generation, embeddings, and semantic search. This role solves the technical problem of making enterprise data usable, governed, and dependable for production AI applications within a secure facility.
Design data architectures and build pipelines, knowledge indexes, and retrieval services for AI applications.
Define data contracts, schemas, and APIs for integrating structured and unstructured data sources.
Maintain data permissions, lineage, quality controls, and monitoring for data freshness and availability.
Develop, test, and operate ETL or ELT pipelines using SQL and Python.
Manage retrieval augmented generation (RAG) components including embeddings, vector indexes, and hybrid search.
Troubleshoot ingestion and retrieval failures while assessing relevance, latency, and scalability.
Translate enterprise requirements into architecture decisions, integration standards, and governance patterns.
What we're looking for
U.S. citizenship is required.
Bachelor's degree and 9+ years of experience designing or implementing enterprise data platforms, Lakehouse architectures, and integrations for structured and unstructured data.
10+ years of experience designing or implementing enterprise data platforms, Lakehouse architectures, and integrations for structured and unstructured data in lieu of a degree.
Experience with Databricks, Spark, Delta Lake, or vector databases.
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 SQL and Python.
Knowledge of retrieval augmented generation, embeddings, vector indexes, and semantic or hybrid search.