Data Engineer

Join GM Performance Power Units onsite in Concord, NC to build AWS-based data pipelines that connect power unit test results to approved engine, build, and component information for analysis.

Responsibilities

• Build and maintain ingestion pipelines for agreed dyno, test rig, instrumentation, and performance sources, supporting batch, file, and streaming patterns as required.
• Retain source files and critical context including timestamps, units, calibration references, run identifiers, and acquisition details.
• Implement validation to surface missing, disputed, or inconsistent context and keep data traceable back to source.
• Create documented datasets and processing pipelines using Python and SQL.
• Prevent duplicates, manage late-arriving data, add checkpoints, and enable controlled replay.
• Link test runs to approved engine, build, and serialize component identities, coordinating with system owners to resolve mismatches and maintain traceable mappings.
• Prepare and publish agreed test and performance summaries from AWS for use by the business and manufacturing data platform.
• Establish controlled data exchanges with the business and manufacturing data engineer, including reference data needed to connect test results to engines, builds, and components.
• Monitor data freshness, pipeline failures, processing performance, and cloud consumption; tune storage, retention, and compute to meet engineering and cost requirements.
• Use source control with peer review, automated checks, and controlled deployments; maintain separate development and production configurations and protect credentials.
• Document pipelines, dependencies, and recovery procedures; train a nominated deputy and contribute to an agreed support and incident response model.

Requirements

• Practical experience building and supporting production data pipelines on AWS, including Amazon S3 and appropriate processing, orchestration, and monitoring services.
• Strong Python and SQL skills, with experience working with large files and/or time-series and 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; ability to work with infrastructure as code and CI/CD practices.
• Knowledge of cloud access controls, service identities, encryption, and secure connectivity, working alongside 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 stakeholders.
• Relevant qualification or equivalent practical experience, demonstrating delivered outcomes.

Technologies

• 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

• Experience in automotive, motorsport, aerospace, manufacturing, or another test-intensive engineering environment.
• Background with dyno or rig data, including signal alignment, timestamp synchronization, calibration context, telemetry formats, and engineering metadata.
• Experience with Parquet or Delta Lake and distributed processing, using AWS services such as Glue, Athena, EMR, Kinesis or equivalent tooling selected for the workload.
• Experience with cross-cloud data exchange, Microsoft Fabric or Power BI integration, and linking test results to PLM, ERP, or lifing records.
• Databricks on AWS experience is beneficial; it is not a prerequisite if the processing requirements justify alternatives.