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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Data Engineer - GM Motorsports - **Company:** General Motors - **Location:** Atlanta, GA, United States - **Salary:** $160,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Amazon Web Services, Data Analysis, Microsoft Azure, Cloud Computing, Computer Programming, Information Engineering, Distributed Systems, Python (Programming Language), Machine Learning, Runbook, Scala (Programming Language), Apache Spark, Kubernetes, Apache Flink, Complex Event Processing, Real Time Data, Apache Kafka, Operational Systems, Data Management, Data Pipelines, Databricks - **Published:** October 16, 2026 - **Apply:** https://www.atlantacareerpath.com/job.asp?id=3451603255&tx=FJ8582FFQ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * Deep expertise in data engineering, distributed systems, cloud platforms, and production software development. * Demonstrated experience setting technical direction and delivering complex solutions spanning multiple teams or functional areas. * Strong programming experience in Python, Java, Scala, or a comparable language. * Experience designing and operating streaming and batch data pipelines at production scale. * Strong knowledge of data modeling, schema evolution, data quality, observability, testing, deployment, and incident response. * Demonstrated ability to work independently on ambiguous problems and make decisions with broad business and technical implications. * Experience influencing stakeholders and gaining adoption of new architectures, processes, or technical practices without relying on direct authority. * Strong communication, collaboration, technical writing, and mentoring skills. What Will Give You a Competitive Edge (Preferred Qualifications) * Experience with Databricks, Apache Kafka, Flink, Spark, Kubernetes, AWS, Azure, or comparable technologies. * Experience with real-time telemetry, event processing, time-series data, or latency-sensitive production workloads. * Experience supporting systems during high-availability or time-critical operating periods. * Experience with data science, machine learning, analytics, or engineering strategy teams. * Experience establishing engineering standards, technical communities of practice, or cross-functional architecture forums. * Interest in motorsports, vehicle systems, race strategy, or performance engineering. This role is expected to operate as a recognized subject-matter expert, develop solutions to broad and complex problems, lead initiatives with cross-functional visibility, and make measurable contributions to functional outcomes. The role will set objectives for technical and operational results, influence decisions across organizational boundaries, and multiply impact through mentoring, reusable practices, and effective collaboration. ## Description The Staff Data Engineer is an expert-level individual contributor who sets technical direction for complex Motorsports data engineering initiatives. This role works independently with broad latitude, applies deep expertise and sound judgment to ambiguous problems, and delivers solutions with measurable impact across teams and functional areas. The role will lead architecture and engineering practices for real-time data ingestion, telemetry, data quality, cloud platforms, and race-day operational systems. The Staff Data Engineer will influence technical decisions across the organization, mentor engineers, and partner with engineering, data science, platform, product, and racing stakeholders to deliver durable business outcomes. What You'll Do * Set technical direction and develop target-state architecture for complex, cross-system data platforms and products. * Lead large, high-visibility initiatives that integrate multiple teams, disciplines, technologies, and stakeholder needs. * Design and implement innovative, scalable solutions for real-time ingestion, telemetry, event and session data, data quality, and analytical products. * Exercise independent judgment to analyze complex problems, select appropriate methods, evaluate tradeoffs, and make durable technical decisions. * Establish and own measurable technical and operational outcomes, including reliability, data quality, throughput, cycle time, recovery, and service-level performance. * Develop standards, policies, methods, and reusable patterns for schemas, data contracts, observability, testing, deployment, security, and recovery. * Lead technical planning, dependency management, risk identification, and sequencing for roadmap and race-critical work. * Anticipate and escalate risks that could affect customers, race operations, delivery commitments, or functional outcomes, while providing actionable recommendations. * Improve operational reliability through incident leadership, root-cause analysis, automation, runbooks, and permanent corrective actions. * Influence adoption of new approaches through data, clear communication, persuasion, and collaboration across teams with differing priorities or practices. * Serve as a technical resource and indirect leader by mentoring engineers, guiding design reviews, and raising engineering capability across the organization. * Partner with stakeholders to understand customer and business needs and translate them into scalable data solutions. * Champion change management and continuously improve engineering processes, tools, and ways of working., * Committing to customers by understanding stakeholder needs and delivering high-quality outcomes. * Innovating and embracing change through experimentation, learning, and continuous improvement. * Speaking fearlessly by raising risks, challenging assumptions respectfully, and offering actionable solutions. * Moving with urgency by focusing on high-impact work, anticipating obstacles, and flagging delays early. * Being inclusive by inviting diverse perspectives and creating space for open dialogue. * Leading as one team by providing context, clarifying ownership, and aligning cross-functional partners. * Owning the outcome by following through on commitments, addressing gaps, and considering enterprise impact. Success measures * Measurable improvement in reliability, observability, data quality, and recovery readiness for critical data products. * More predictable delivery through improved architecture, dependency management, sequencing, and flow metrics. * Reduced aging, carryover, rework, and recurring operational toil. * Documented adoption of reusable data engineering standards and target-state architecture. * Stronger engineering capability through mentoring, design reviews, and knowledge sharing. * Demonstrated impact on Motorsports functional outcomes and race-day readiness., This role is categorized as remote. This means the selected candidate may be based anywhere in the country of work and is not expected to report to a GM worksite unless directed by their manager.