ResidencyAll applicants must currently reside in the United States.OverviewBigBear.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 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.
- Proficiency in Python or Java for building data services and transformation logic.
- Solid SQL skills and working familiarity with NoSQL data stores.
- Experience with REST/API frameworks and building maintainable, well-tested integration services.
- Graph Database experience
- Hands-on experience producing/consuming events in Kafka (producers/consumers) or an equivalent event streaming platform
- IC/DoD experience
Tools & Technical Skills- Python or Java
- REST/API frameworks
- Kafka producers/consumers
- SQL and NoSQL databases
- Cypher or SPARQL
- Athena
- Lambda
- Glue
- Neo4J
- Neptune
- AWS DMS (Database Migration Service)
What we'd like you to have- 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.
Pay transparencyPlease note the targeted compensation range is provided as an estimate, and any actual compensation offer may vary depending on the needs of the company, or an applicant's skillset, competencies, experience, education, certifications, location, or other factors. The estimated range does not include the value of any benefits offered.
