Data Engineer

Overview

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 will require travel in the DMV area.

What you will do: * 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 you need to have: * Clearance: Must maintain an active Top Secret security clearance * Bachelor's Degree and 5 to 8 years of experience with a Bachelor's degree, equivalent experience may be accepted in lieu of a degree * 3+ years of experience in data engineering, including building production-grade ingestion and transformation pipelines. * Strong experience with API integ