Data Engineer
BigBear Inc
Residency
All applicants must currently reside in the United States
Overview
The Data Engineer builds and maintains the source adapters and normalization logic that translate raw data from disparate systems into a common risk-signal schema. This role 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.
Hands-on experience producing/consuming events in Kafka (producers/consumers) or an equivalent event streaming platform
IC/DoD experience
What we'd like you to have
Tools & Technical Environment (Preferred/Used)
Python or Java
REST/API frameworks
Kafka producers/consumers
SQL and NoSQL databases
Key Behavioral Competencies
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 transparency
Please 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.
About BigBear.ai
BigBear.ai is a leading provider of AI-powered decision intelligence solutions for national security, supply chain management, and digital identity. Customers and partners rely on Bigbear.ai’s predictive analytics capabilities in highly complex, distributed, mission-based operating environments. Headquartered in McLean, Virginia, BigBear.ai is a public company traded on the NYSE under the symbol BBAI. For more information, visit and follow BigBear.ai on LinkedIn: @BigBear.ai and X: @BigBearai.
BigBear.ai is an Equal opportunity employer all protected groups, including protected veterans and individuals with disabilities.
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