Senior Data Engineer
Role details
Job location
Tech stack
Job description
This role is categorized as hybrid. This means the successful candidate is expected to report to Austin Technical Center in Austin, TX or GM Global Tech Center in Michigan, MI three times per week, at minimum [or other frequency dictated by the business if more than 3 days]., As a Data Engineer, you will design, build, and optimize industrialized data assets and data pipelines in support of Business Intelligence and Advanced Analytics objectives. In this role within the Intelligent Manufacturing organization under Data Engineering Software, you will deliver high-quality, scalable solutions that meet both functional and non-functional business requirements. You will contribute to projects across databases, streaming technology, CI/CD, and cloud platforms.
The Intelligent Manufacturing teams are responsible for ideating, incubating, and delivering new plant data solutions for General Motors Manufacturing and our partners. We integrate with business and IT teams to develop real-time solutions that leverage plant floor data to improve decisions, plant asset maintenance, safety, and operational performance, as well as Vehicle Build Data.
This is a senior-level role that blends strong data engineering skills with modern software engineering practices. You will help lead and deliver innovative, scalable, and maintainable data-driven solutions-writing high-quality, tested, production-ready code that meets customer needs and scales without rework. You will work in a collaborative, cross-disciplinary environment, handle complex challenges, contribute to architectural discussions, and help shape solutions that improve performance, scalability, and maintainability, while also ensuring alignment with business priorities.
What You'll Do
- Assemble large, complex data sets that meet functional and non-functional business requirements.
- Identify, design, and implement process improvements, including automation, data delivery optimization, and infrastructure redesign for scalability.
- Lead and deliver data-driven solutions across multiple languages, tools, and technologies.
- Contribute to architecture discussions, solution design, and strategic technology adoption.
- Build and optimize highly scalable data pipelines incorporating complex transformations and efficient code.
- Design and develop new source system integrations from varied formats (files, database extracts, APIs).
- Design and implement solutions for delivering data that meets SLA requirements.
- Work with operations teams to resolve production issues related to the platform.
- Apply best practices such as Agile methodologies, design thinking, and continuous deployment.
- Develop tooling and automation to make deployments and production monitoring more repeatable.
- Collaborate with business and technology partners, providing leadership, best practices, and coaching.
- Mentor peers and junior engineers; educate colleagues on emerging industry trends and technologies.
Requirements
- Bachelor's degree in Computer Science, Software Engineering, or related field, or equivalent experience
- 7+ years of data engineering/development experience, including Python or Scala, SQL, and relational/non-relational data storage. (ETL frameworks, big data processing, NoSQL)
- 3+ years of experience in distributed data processing (Spark) and container orchestration (Kubernetes)
- Proficiency in data streaming in Kubernetes and Kafka
- Experience with cloud platforms - Azure preferred; AWS or GCP also considered.
- Solid understanding of CI/CD principles and tools
- Familiarity with big data technologies such as Hadoop, Hive, HBase, Object Storage (ADLS/S3), Event Queues.
- Strong understanding of performance optimization techniques such as partitioning, clustering, and caching
- Proficiency with SQL, key-value datastores, and document stores
- Familiarity with data architecture and modeling concepts to support efficient data consumption
- Advanced understanding of data normalization and denormalization techniques.
- Translate enterprise requirements into effective data models and address project requirements when applicable.
- Design, build, and optimize scalable batch and streaming data pipelines using Databricks (Apache Spark, Delta Lake) to support Medalion Architecture.
- Contribute to the design and operational management of a cloud-native data platform on Azure , integrating services such as Azure Data Lake Storage, Event Hubs, Azure SQL, and AKS.
- Monitor and troubleshoot data pipelines and platform workloads. Optimize Spark jobs, cluster configurations, and SQL warehouses to improve performance
- Strong collaboration and communication skills; ability to work across multiple teams and disciplines.
What Can Give You a Competitive Advantage (Preferred Qualifications)
- Master's degree in Computer Science, Software Engineering, or related field
- Knowledge of data governance, metadata management, or data quality/observability
- Familiarity with schema design and data contracts
- Experience handling various file formats (video, audio, image)
- Experience with Databricks, Snowflake, or similar platforms
Benefits & conditions
- The expected base compensation for this role is: $129.400 - $168,650. Actual base compensation within the identified range will vary based on factors relevant to the position.
- Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
- Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.