Data Engineer

GM Performance Power Units is seeking a Data Engineer to build and operate AWS-based data pipelines that support power unit test and performance activities. The role focuses on turning engineering outputs into data that is accessible, traceable, and ready for analysis, with a clear emphasis on connecting test results to approved engine, build, and component identities.

This onsite position in Concord, NC involves close collaboration with teams including Test, Performance, Controls, IT, and business or manufacturing data roles. You will help ensure that the right reference context accompanies each dataset, and you will publish agreed test and performance summaries from AWS for controlled exchange into the business and manufacturing data platform.

What you’ll do

• Build and maintain ingestion pipelines for agreed dyno, test rig, instrumentation, and performance sources, supporting batch, file, and streaming patterns when required.
• Preserve source files and relevant timestamps, units, calibration references, run identifiers, and acquisition context, with validation that makes missing or disputed context visible.
• Develop documented datasets and processing pipelines using Python and SQL, including duplicate prevention, late-data handling, checkpoints, and controlled replay.
• Link test runs to approved engine, build, and serialize component identities, working with system owners to resolve mismatches and maintain traceable mappings.
• Prepare and publish agreed test and performance summaries from the AWS environment for use by the business and manufacturing data platform, establishing controlled data exchanges and the reference data needed to connect results to engines, builds, and components.
• Monitor data freshness, pipeline failures, processing performance, and cloud consumption, tuning storage, retention, and compute to meet agreed engineering and cost requirements.
• Use source control, peer review, automated checks, and controlled deployment, maintaining separate development and production configurations and protecting credentials.
• Document pipelines, dependencies, and recovery procedures, train a nominated deputy, and contribute to an agreed support and incident response model.

Key requirements

• Practical experience building and supporting production data pipelines on AWS, including Amazon S3 and relevant processing, orchestration, and monitoring services.
• Strong Python and SQL skills, including experience working with large files, time-series data, or other complex engineering datasets.
• Understanding of incremental processing, idempotency, schema changes, data quality controls, and recovery from partial failure.
• Experience with Git, code review, testing, and repeatable deployments, with ability to work with infrastructure as code and CI/CD practices.
• Knowledge of cloud access controls, service identities, encryption, and secure connectivity, with collaboration across infrastructure and security specialists.
• Ability to diagnose failures, explain cost drivers, and document a service so another engineer can maintain it.
• Ability to translate engineering requirements into clear data contracts and acceptance tests, and communicate effectively with technical and business colleagues.
• Relevant qualification or equivalent practical experience, with evidence of delivered outcomes.

Technology focus

• AWS, Amazon S3, Python, SQL
• Git, infrastructure as code, CI/CD
• Microsoft Fabric, Power BI, Parquet, Delta Lake
• Glue, Athena, EMR, Kinesis, Databricks
• Power BI integration

Benefits

• Bonus scheme
• Private healthcare
• 401(k) plan including company match
• Employer-paid life insurance and disability coverage
• 32 days annual leave (including holidays)
• Free financial advisor access
• Employee Assistance Programs (EAP)
• Subsidized on-site dining
• On-site gym
• Discounted GM vehicle purchase/lease program
• Free on-site EV charging for personal GM vehicles

Preferred qualifications

• Automotive, motorsport, aerospace, manufacturing, or another test-intensive engineering environment.
• Experience with dyno or rig data, including signal alignment, timestamp synchronization, calibration context, telemetry formats, and engineering metadata.
• Experience with Parquet or Delta Lake, distributed processing, and AWS services such as Glue, Athena, EMR, Kinesis, or equivalent tools selected for the workload.
• Cross-cloud data exchange, Microsoft Fabric or Power BI integration, and linking test results to PLM, ERP, or lifing records.
• Databricks on AWS where justified by processing requirements. Databricks experience is beneficial rather than a prerequisite.