What to Expect
We're seeking a highly skilled and collaborative Senior Data Engineer to design and implement a cutting-edge data platform while leading the development of data pipelines of our transformation layer and dimensional data models that power analytics across our energy data platform — including Industrial, Residential, Supercharger, and Solar products. You'll own the DBT-based transformation framework, architect well-modeled SILVER and GOLD datasets, and partner with data engineers, analysts, and ML engineers to turn raw fleet, application, and warehouse data into trusted, query-optimized models. Your role involves both modernizing how we transform and model data — introducing DBT as the foundation for our transformation workflows and establishing strong modeling standards — and contributing to the broader data platform: optimizing pipelines, improving monitoring, and supporting reporting infrastructure. You'll create analytics-ready datasets that track energy products throughout their lifecycle, from creation to deployment, usage, maintenance, and replacement. Working cross-functionally with teams across our energy divisions, you'll leverage your expertise in analytics engineering and data modeling to build scalable, well-tested, and well-documented data products that accelerate decision-making.
What You'll Do-
Design and implement robust, scalable batch and streaming pipelines for processing terabyte-scale data using Apache Spark, Kafka, and other modern data technologies
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Design and implement scalable transformation pipelines using DBT (Core) on top of Spark/Iceberg, and migrate existing SparkSQL/PySpark logic into modular, tested, version-controlled DBT projects
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Architect dimensional and denormalized data models (fact/dimension, SCD Type 2, wide analytical tables) that serve BI, ML, and operational use cases
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Establish modeling standards, naming conventions, testing patterns, and documentation practices (DBT tests, exposures, lineage)
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Develop aggregate and summary tables for various engineering teams that span across multiple product lines and geographies
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Design and develop new systems and tools to enable cross-functional teams to consume and understand data faster
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Implement and maintain CI/CD pipelines for data and transformation applications, and research and incorporate emerging data infrastructures, tools, and technologies
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3+ years of professional experience in data engineering, analytics engineering with a proven track record of designing and shipping production data platforms that serve analytics, BI, and ML workloads at scale
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Production experience with DBT, including incremental models, snapshots, macros, tests, packages, and DBT-based CI/CD workflows
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Strong dimensional modeling expertise (star/snowflake schemas, medallion architecture, SCD patterns, ) with expert-level SQL and deep understanding of query optimization and warehouse internals
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Strong programming foundation in Python for building data pipelines, transformations, and developer tooling; additional fluency in Scala or Rust is highly valued
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Expert-level experience with Apache Spark (PySpark, Spark SQL, Spark Streaming); experience with Apache Flink and Kafka is a plus
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Hands-on experience with modern open table formats (Delta Lake, Apache Iceberg, or Apache Hudi)
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Experience with containerization and orchestration (Docker, Kubernetes)
Tesla is an Equal Opportunity / Affirmative Action employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws.
Tesla is also committed to working with and providing reasonable accommodations to individuals with disabilities. Please let your recruiter know if you need an accommodation at any point during the interview process.
