Remote Data Ingestion Engineer: ETL & Streaming

Bigbear.ai; McLean (VA); On-site; USD 120,000 - 160,000; Full time

Job summary

BigBear.ai is hiring Data Engineers to build adapters and normalization logic that translate heterogeneous data into a common risk-signal schema. The role focuses on reliable ingestion and transformation for downstream scoring and adjudication workflows.

Remote, with possible travel within the DMV area, and residency in the United States is required. You will design and maintain ingestion pipelines, ensure data quality, and collaborate with data architecture and SMEs on data contracts and

Qualifications

• Bachelor's degree (or higher) with 8–10 years' experience, or Master's with 6–8 years.
• 3–5 years in data engineering, building production-grade ingestion and transformation pipelines.
• Strong API integrations and ETL/ELT development in complex environments.

Responsibilities

• Build source adapters/connectors to ingest data from APIs, legacy systems, databases, and event streams.
• Develop normalization and mapping logic to translate source fields into a common risk-signal schema.
• Implement ETL/ELT pipelines with testing, observability, and error handling.
• Produce and consume streaming events (e.g., Kafka) for near-real-time signal delivery.
• Define and maintain data contracts, mappings, and lineage from source to normalized signal.

Skills

SQL

Python

ETL/ELT

Data ingestion

API integrations

Kafka

Data normalization

Education

Bachelor's Degree in a relevant field

Master's Degree in a relevant field

Tools

AWS DMS

Graph Database

REST API frameworks

Job description

BigBear.ai is hiring Data Engineers to build adapters and normalization logic that translate heterogeneous data into a common risk-signal schema. The role focuses on reliable ingestion and transformation for downstream scoring and adjudication workflows.

Remote, with possible travel within the DMV area, and residency in the United States is required. You will design and maintain ingestion pipelines, ensure data quality, and collaborate with data architecture and SMEs on data contracts and