This role focuses on building enterprise-grade data foundations for AI systems using authorized information. You will design architectures, implement pipelines and retrieval services, and help ensure data reliability, governance, and retrieval quality in production.
Key Responsibilities
- Design enterprise data architectures and build the pipelines, knowledge indexes, and retrieval services that ground agents in authorized enterprise information.
- Partner 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, collaborating with AI Engineering to improve application retrieval behavior and response quality.
- Support architecture and governance decisions for architecture-focused roles, or pipeline, connector, and service implementation for engineering-focused roles.
- Implement and maintain ingestion, transformation, indexing, synchronization, and retrieval services.
Required Qualifications
- 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 supporting 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 evaluate 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.
Preferred Experience
- 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.
Technology Focus
SQL, Python, ETL, ELT
Compensation
The projected compensation range for this position is $77,600.00 to $176,000.00 (annualized USD).
Benefits
- Health, life, disability, financial, and retirement benefits
- Paid leave
- Professional development
- Tuition assistance
- Work-life programs
- Dependent care
- Recognition awards program
Work Model and Location
McLean, VA (onsite)
Work will primarily be performed full-time at a customer facility, where employees will collaborate directly with colleagues and customers as required by the role.
Vetting and Eligibility
Applicants selected will be subject to a government investigation and may need to meet eligibility requirements of the U.S. government client.
Identity and Interview Policies
- Applicants are expected to be on camera during interviews and assessments.
- We reserve the right to take your picture to verify your identity and prevent fraud.
- The use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided.
Non-Discrimination Commitment
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.
