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. In this onsite role in Concord, NC, you will help make engineering data accessible and traceable, connecting test results to approved engine, build, and component identities for analysis and business use.

What you’ll do

• Build and maintain ingestion pipelines for agreed dyno, test rig, instrumentation, and performance sources, using batch, file, or streaming patterns when the requirements call for them.
• Preserve source files and key timestamps, units, calibration references, run identifiers, and acquisition context, then implement validation to surface missing or disputed context.
• Create 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 serialized component identities, collaborating with system owners to resolve mismatches while keeping mappings traceable.
• Prepare and publish agreed test and performance summaries from the AWS environment for use by the business and manufacturing data platform, supporting controlled data exchanges and the reference data needed to connect test results to engines, builds, and components.
• Monitor data freshness, pipeline failures, processing performance, and cloud consumption, tuning storage, retention, and compute to meet 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.

Required experience

• Practical experience building and supporting production data pipelines on AWS, including Amazon S3 and the supporting processing, orchestration, and monitoring services.
• Strong Python and SQL skills, including work with large files, time-series data, and complex engineering datasets.
• Understanding of incremental processing, idempotency, schema changes, data quality controls, and recovery from partial failures.
• Experience with Git, code review, testing, and repeatable deployments, including infrastructure as code and CI/CD practices.
• Knowledge of cloud access controls, service identities, encryption, and secure connectivity, partnering with 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, with effective communication across technical and business colleagues.
• Relevant qualification or equivalent practical experience, with evidence of delivered outcomes.

Technologies you may work with

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

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

• Experience in automotive, motorsport, aerospace, manufacturing, or another test-intensive engineering environment.
• Knowledge of dyno or rig data topics such as 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.
• Experience with cross-cloud data exchange and Microsoft Fabric or Power BI integration, including linking test results to PLM, ERP, or lifing records.
• Databricks on AWS where justified by processing needs, with Databricks experience beneficial rather than required.