Data Engineer

As a Data Engineer on the global EHS&S data team, you'll turn raw operational data into trusted, well-modeled datasets that support Tesla's Environmental, Health, Safety, and Security (EHS&S) goals. Your main focus will be training data. You'll own the dbt transformation and modeling layer behind our training dashboard, one of our most used dashboards. You'll also partner across functions to deliver accurate, reliable metrics for internal reporting. The right candidate sits between engineering and analytics: a hands-on builder who cares about data modeling, metric definitions, and data quality, and who can turn ambiguous business questions into clear, reusable data products.

- Partner with EHS&S stakeholders and business leaders to understand their processes, find
the questions that matter most, and turn them into clear metrics and data requirements
- Design, build, and maintain dimensional models (facts, dimensions, marts) in dbt that serve as the single source of truth for safety, training, environmental, and security reporting
- Define, document, and govern KPIs so they are calculated the same way across dashboards, reports, and teams
- Own the Power BI semantic models behind the global Training dashboard and other high-visibility
dashboards, optimizing for performance, usability, and self-service
- Model training data to track training completion, overdue assignments, and certification
status across regions, sites, and job roles, while being able to analyze trends and data gaps, and share insights that help EHS&S teams prioritize actions an measure their impact
- Support internal and external reporting with accurate, reconcilable, and auditable metrics and curate and structure datasets that support LLM-driven use cases, such as incident classification and corrective action generation
- Implement data quality tests, validation checks (e.g., dbt tests, soda.io), and documentation so stakeholders trust the numbers
- Enable self-service analytics by building intuitive datasets, writing clear documentation, and training business users
- Apply software engineering best practices to analytics code, including version control, code review, and modular, reusable dbt model and be able to mentor teammates and analysts on data modeling, SQL, dbt, and Power BI best practics

- 3 or more years in analytics engineering, data analytics, BI development, or a related data role, and a Degree in Computer Science, Mathematics, Statistics, Data Science, Economics, or equivalent experience
- Expert SQL, including complex joins, window functions, CTEs, and query performance tuning
- Hands-on dbt in production, including models, tests, macros, snapshots, and documentation
- Strong dimensional modeling, including Kimball, slowly changing dimensions, and metric or semantic layer design
- Proven work building data models and dashboards in a BI tool, with Power BI preferred, plus the ability to explore data, find trends and anomalies, and turn findings into clear recommendations
- Strong business sense: turn ambiguous stakeholder questions into defined data requirements, govern KPIs, and reconcile metrics across sources and reports
- Clear communication and data storytelling for technical and non-technical audiences, including senior leadership, with a track record of delivering end-to-end work independently, and an eagerness to learn and make an impact in environmental, health, safety, and security