Database Engineer

Bigbear.ai • McLean, VA

7d • Remote

About The Position

BigBear.ai is hiring Data Engineers to build and maintain the source adapters and normalization logic that translate raw data from disparate systems into a common risk-signal schema. This position focuses on reliable ingestion and transformation—turning heterogeneous legacy inputs (APIs, feeds, databases, files, and event streams) into consistent, high-quality signals that downstream scoring and adjudication workflows can trust. This position is remote but may require travel in the DMV area.

Requirements

• Must maintain an active Top Secret security clearance
• Bachelor's Degree and 8 to 10 years of experience; Master's Degree and 6 to 8 years of experience
• 3–5 years of experience in data engineering, including building production-grade ingestion and transformation pipelines.
• Strong experience with API integrations and ETL/ELT development in complex environments.
• Experience integrating heterogeneous and/or legacy systems with inconsistent schemas and data quality.
• Experience with REST/API frameworks and building maintainable, well-tested integration services.

Nice To Haves

• Engineering discipline: writes maintainable, testable code and builds robust pipelines that handle edge cases.
• Curiosity and persistence: digs into messy source data and drives it to consistent outcomes.
• Collaboration: works effectively across data architecture, scoring/analytics, and application teams.
• Operational mindset: builds pipelines that are observable, debuggable, and supportable in production.
• Solid SQL skills and working familiarity with NoSQL data stores.
• Proficiency in Python or Java for building data services and transformation logic.
• Hands-on experience producing/consuming events in Kafka (producers/consumers) or an equivalent event streaming platform

Responsibilities

• Build source adapters/connectors to ingest data from APIs, legacy systems, databases, and event streams
• Develop normalization and mapping logic to translate source-specific fields into the common risk-signal schema (including validation, enrichment, and standardization)
• Implement ETL/ELT pipelines with strong engineering rigor: testing, observability, error handling, retries, and backfills
• Produce and consume streaming events (e.g., Kafka topics) to support near-real-time signal delivery and downstream processing
• Partner with data architecture and domain SMEs to define and maintain data contracts, mappings, and lineage from source to normalized signal
• Ensure data quality and consistency (deduplication patterns, schema evolution handling, and reconciliation against source systems)
• Optimize pipeline performance and reliability (throughput, latency, and scalable processing patterns)
• Create and maintain technical documentation for adapters, transformations, and operational runbooks
• Some travel may be required within the DMV area

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level