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EDAI Data Engineering Manager
| Company | General Motors |
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| Location | Austin, TX, USA |
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| Salary | $117800 – $194300 |
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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 (Master’s preferred) in Computer Science, Data Engineering, Information Systems, or a related technical field.
- 3+ years of experience managing data engineering or software engineering teams, with a focus on building and maintaining production-grade data infrastructure.
- 7+ years of hands-on experience in data engineering, including designing, developing, and optimizing large-scale data pipelines and ETL/ELT workflows.
- Proficiency in Python, SQL, and distributed data processing frameworks such as PySpark.
- Experience with modern data platforms and tools such as Airflow, dbt, Snowflake, Databricks, Kafka, or similar.
- Experience with cloud architecture systems such as Azure, AWS, GCP, etc.
- Ability to communicate long-term vision and roadmap milestones through compelling storytelling and data-driven insights to influence leadership and ensure strategic alignment.
- Strong understanding of data modeling, data warehousing, and data governance best practices.
- Demonstrated success in collaborating with cross-functional teams and translating business requirements into scalable data solutions.
- Strong leadership and mentoring skills, with a track record of developing high-performing engineering teams.
Responsibilities
- Lead and mentor a multidisciplinary team of Data Engineers, Scrum Leads, Architects, and Product Managers to build scalable analytic data foundations.
- Architect, design, and implement data ingestion, transformation, and orchestration workflows using modern data engineering tools and cloud-native technologies.
- Ensure high data quality, reliability, and availability across batch and real-time data processing systems.
- Establish and promote best practices in data engineering, including version control, testing, documentation, and CI/CD for data pipelines.
- Contribute to the evolution of the data architecture and help define long-term strategies for data infrastructure and tooling.
Preferred Qualifications
- Master’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field (preferred).
