Data Engineer, Lead

Booz Allen Hamilton•McLean, VA

3h•$77,600 - $176,000•Hybrid

About The Position

The Data Engineer, Lead will make enterprise data and knowledge usable, governed, and dependable for AI applications. The role designs data architectures and builds pipelines, knowledge indexes, and retrieval services that ground agents in authorized enterprise information. The position works with source-system owners and engineering teams to define data contracts, integrate structured and unstructured sources, and maintain permissions, lineage, quality, and freshness. The engineer owns the reliability of data and retrieval services and partners with AI Engineering on application retrieval behavior and response quality. Engineer positions emphasize pipeline, connector, and service implementation, while architect positions emphasize architecture and governance decisions. U.S. citizenship is required due to the nature of work performed at the facility. Applicants selected will be subject to a government investigation and may need to meet U.S. government client eligibility requirements. The position is hybrid and based in McLean, Virginia. The projected annualized compensation range is $77,600 to $176,000. Booz Allen describes benefits that include health, life, disability, financial and retirement benefits, paid leave, professional development, tuition assistance, work-life programs, dependent care, and recognition awards. Eligibility for benefit programs generally applies to full-time and part-time employees regularly working at least 20 hours per week, with some offerings limited for employees below that threshold.

Requirements

Experience designing or implementing enterprise data platforms, lakehouse architectures, and integrations for structured and unstructured data.
Experience with SQL and Python, including developing, testing, and operating ETL or ELT pipelines.
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.
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.
Ability to implement and maintain ingestion, transformation, indexing, synchronization, and retrieval services.
Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements.
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 data architectures for enterprise data and knowledge used by AI applications.
Build pipelines, knowledge indexes, and retrieval services that ground agents in authorized enterprise information.
Work with source-system owners and engineering teams to define data contracts.
Integrate structured and unstructured data sources.
Maintain source permissions, lineage, data quality, and freshness.
Own the reliability of data and retrieval services.
Partner with AI Engineering on application retrieval behavior and response quality.
Implement and maintain ingestion, transformation, indexing, synchronization, and retrieval services.
Troubleshoot ingestion and retrieval failures and assess relevance, latency, scalability, and data quality.
Translate enterprise requirements into architecture decisions, integration standards, and governance patterns.