BigBear.ai
From BigBear.ai's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
In this role, you’ll build scalable, secure, and reliable data pipelines that turn complex, raw data into trusted, analytics-ready datasets.
Residency
All applicants must reside in the United States at the time of application.
Overview
BigBear.ai is hiring a Data Engineer to join our Data Architecture & Engineering team supporting mission-critical outcomes. In this role, you’ll build scalable, secure, and reliable data pipelines that turn complex, raw data into trusted, analytics-ready datasets. You’ll partner closely with engineers, analysts, and stakeholders to deliver data products that power applications, reporting, and decision advantage in high-impact environments.
Design and build end-to-end pipelines to ingest, transform, and deliver structured and unstructured data, aligned with modern ETL/ELT best practices.
Develop and operate ETL processes that emphasize data quality, consistency, performance, and repeatability across environments.
Leverage AWS services (e.g., S3, Lambda, Glue, Redshift) to enable scalable, secure data processing and storage.
Create and maintain data assets such as schemas, transformation logic, and documentation (data dictionaries, lineage/dataflows, runbooks).
Monitor and support production data systems, ensuring integrity, fault tolerance, redundancy, and strong operational hygiene.
Collaborate across teams to deliver new data features and improvements, including documentation of capabilities and technical support procedures.
Support database engineering tasks including indexing, access controls, optimization, and performance tuning.
Contribute in an Agile environment, delivering iterative value and responding quickly to evolving mission needs.
Build and integrate APIs to enable secure, interoperable access to data across systems.
Work with cloud messaging APIs and implement push-notification mechanisms when applicable.
Continuously improve tooling, patterns, and practices to strengthen data engineering maturity and mission outcomes.
Active TS/SCI clearance (must possess and maintain).
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline.
4–8 years of experience in data engineering, ETL development, and/or database administration.
Strong hands-on experience with AWS and cloud-based data integration.
Proven ability to design ETL/ELT architecture and build reliable data pipelines at scale.
Solid foundation in database design and performance (schema design, indexing, optimization).
Ability to transform raw data into analytics- and reporting-ready formats with clear quality standards.
Familiarity working in Agile teams and collaborative engineering environments.
Strong troubleshooting skills, attention to detail, and comfort supporting production data systems.
Experience with cloud messaging APIs and push notification systems.
Deeper hands-on experience with database administration and performance tuning.
Demonstrated curiosity and initiative in adopting new tools/approaches to solve complex problems.
Experience supporting operations/production support for data platforms.
Familiarity with national security or other mission-driven data environments.
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.
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.
Design and build end-to-end pipelines to ingest, transform, and deliver structured and unstructured data, aligned with modern ETL/ELT best practices.
Develop and operate ETL processes that emphasize data quality, consistency, performance, and repeatability across environments.
Leverage AWS services (e.g., S3, Lambda, Glue, Redshift) to enable scalable, secure data processing and storage.
Create and maintain data assets such as schemas, transformation logic, and documentation (data dictionaries, lineage/dataflows, runbooks).
Monitor and support production data systems, ensuring integrity, fault tolerance, redundancy, and strong operational hygiene.
Collaborate across teams to deliver new data features and improvements, including documentation of capabilities and technical support procedures.
Support database engineering tasks including indexing, access controls, optimization, and performance tuning.
Contribute in an Agile environment, delivering iterative value and responding quickly to evolving mission needs.
Build and integrate APIs to enable secure, interoperable access to data across systems.
Work with cloud messaging APIs and implement push-notification mechanisms when applicable.
Continuously improve tooling, patterns, and practices to strengthen data engineering maturity and mission outcomes.
Active TS/SCI clearance (must possess and maintain).
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline.
4–8 years of experience in data engineering, ETL development, and/or database administration.
Strong hands-on experience with AWS and cloud-based data integration.
Proven ability to design ETL/ELT architecture and build reliable data pipelines at scale.
Solid foundation in database design and performance (schema design, indexing, optimization).
Ability to transform raw data into analytics- and reporting-ready formats with clear quality standards.
Familiarity working in Agile teams and collaborative engineering environments.
Strong troubleshooting skills, attention to detail, and comfort supporting production data systems.
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