Database Engineer

Talentify; McLean (VA); Remote; USD 140,000 - 190,000; Full time

Job summary

BigBear.ai is seeking Data Engineers to build source adapters and normalization logic translating heterogeneous data into a common risk-signal schema. You will ingest from APIs, feeds, databases, files, and event streams, ensuring reliable ETL/ELT pipelines and near-real-time signal delivery.

This role is remote with potential DMV-area travel; active Top Secret clearance and 8–10 years of data engineering experience or 6–8 with master's, plus strong API, Kafka, SQL/NoSQL skills are required.

Qualifications

• 8–10 years of data engineering experience with production-grade ingestion and transformation pipelines.
• Strong experience with API integrations and ETL/ELT in complex environments.
• Experience integrating heterogeneous/legacy systems with inconsistent schemas and data quality.
• Proficiency in Python or Java for building data services and transformation logic.
• Hands-on experience producing/consuming events in Kafka or equivalent 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 a common risk-signal schema.

Skills

Data engineering

API integrations

Python/Java

Kafka

SQL/NoSQL

Education

Bachelor's Degree

Master's Degree

Tools

AWS DMS

Graph Database

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 may 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.
• Experience with REST/API frameworks and building maintainable, well-tested integration services.

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.
• 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

Tools & Technical Skills

• AWS Certification
• Graph Database experience
• SQL and NoSQL databases
• AWS DMS (Database Migration Service)

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.

• 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.
• Experience with REST/API frameworks and building maintainable, well-tested integration services.
• 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