Data Engineer

Bigbear.ai; McLean (VA); Hybrid; USD 120,000 - 170,000; Full time

Job summary

BigBear.ai is hiring Data Engineers to build and maintain adapters and normalization logic translating heterogeneous inputs into a common risk-signal schema. You’ll ensure reliable ingestion, validation, enrichment, and streaming for downstream scoring and adjudication workflows.

Responsibilities include ETL/ELT development, near-real-time streaming with Kafka, and collaboration with data architecture and SMEs to define data contracts and lineage.

Qualifications

• Bachelor's degree required; advanced degree preferred.
• 8–10 years of data engineering experience; 6–8 years with Master’s degree.
• Experience building production-grade ingestion/transformation pipelines.
• Strong API integration and ETL/ELT development skills.

Responsibilities

• Build source adapters/connectors to ingest data from APIs, legacy systems, databases, and event streams.
• Develop normalization/mapping logic to translate fields into a common risk-signal schema.
• Implement ETL/ELT pipelines with testing, observability, error handling, retries, and backfills.
• Produce/consume streaming events (Kafka) for near-real-time signal delivery.
• Define data contracts, mappings, and lineage from source to normalized signal.
• Ensure data quality, deduplication, schema evolution handling, and reconciliation.
• Optimize pipeline performance and reliability (throughput/latency).
• Create and maintain technical runbooks and docs.

Skills

Data engineering

Collaboration

Operational mindset

Education

Bachelor's degree

Master's degree

Tools

Python or Java

REST/API frameworks

Kafka producers/consumers

SQL and NoSQL databases

Cypher or SPARQL

Athena

Lambda

Glue

Neo4J

Neptune

AWS DMS

Job description

Residency

All applicants must currently reside in the United States.

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 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 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 https://bigbear.ai/ 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.