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Staff Data Engineer – Commercial Software
| Company | General Motors |
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| Location | Mountain View, CA, USA |
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| Salary | $186200 – $285300 |
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| Type | Full-Time |
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| Degrees | Bachelor’s |
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| Experience Level | Senior, Expert or higher |
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Requirements
- Bachelor’s Degree or equivalent experience in computer science, data science, engineering, or related quantitative field
- 7+ years of industry experience developing, implementing, and maintaining solutions for Big Data or data warehousing systems
- 5+ years of industry experience working in a cloud environment (Azure preferred)
- 5+ years’ experience working with SQL query authoring for automated data transformation (familiarity with dbt preferred, but not required)
- 3+ years of experience developing streaming data processing pipelines (use of Spark/pySpark preferred)
- Basic understanding of machine learning/statistical learning principles
- Experience implementing and maintaining data workflow orchestration and integration tools (e.g. Airflow/Astro, Prefect, dbt cloud, etc.)
- Understanding of and experience with application of data quality tools integrated with CI/CD automation frameworks in functional deployment environments (e.g., Github Actions/Azure DevOps pipelines)
- Familiarity with data quality testing frameworks (Great Expectations, Deequ)
- Self-driven with an interest in on-the-job learning.
Responsibilities
- Designing, developing, and maintaining efficient data workflows and associated cloud infrastructure that support GM Commercial Software’s core analytic products and services
- Developing and optimizing streaming data pipelines, ideally utilizing Spark or similar technologies
- Working closely with data scientists, DevOps, and software engineers to automate pipelines that process streaming vehicle telemetry data, ensuring real-time data processing and transformation
- Transforming data within our data lakehouse into deployable data models that power our automated fleet insights, visualizations, and emerging machine learning, optimization, and AI applications
- Setting up ELT pipelines to handle massive volumes of spatiotemporal data
- Designing enterprise data warehouse models
- Implementing robust data quality tests
- Leveraging data pipeline-as-code integration systems
Preferred Qualifications
- Master’s Degree in computer science, data science, engineering, or related quantitative field
- Familiarity with enterprise warehouse data modeling techniques (e.g., Kimball)
- Experience integrating simulation systems with distributed, data-intensive processing or analytics applications
- Working familiarity with terraform
- Domain knowledge in automotive systems
- Engagement with modern data stack community (open source and commercial)
- High degree of attention to software craftsmanship and professionalism
- Experience working with containerization technologies and orchestration platforms (specifically Docker and Kubernetes)
