Tesla

Makes electric cars, robotaxis and grid batteries

Data Engineer - Designx

Data EngineeringConfirmed live in the last 24 hours

Full-timePalo Alto, CA, USANo salary listedIn-person

About the Job

Requirements

A degree in computer science or equivalent, and 3+ years of professional experience as a data engineer or backend engineer building large-scale data platforms.
Expertise in Python and SQL for building performant queries, data models, and pipelines at scale, such as PostgreSQL, MySQL, SQL Server, or a data lake.
Experience across the full data lifecycle, including sourcing, ingestion, transformation, analytics, and visualization, with the ability to own projects end-to-end.
Hands-on experience using data processing and orchestration frameworks such as Spark, Airflow, dbt, or Dagster in production at scale.
Production experience with real-time or distributed streaming systems such as Kafka, Spark Streaming, or Flink, handling high-volume, high-resolution time-series data.
Proven experience building and deploying time-series forecasting models in production on high-resolution operational data.
Hands-on experience formulating and solving mathematical optimization problems, including mixed-integer linear programming and convex or non-convex optimization, using tools such as PuLP, Pyomo, Gurobi, or CVXPY.
Experience integrating and reconciling data across multiple source systems with conflicting schemas or update cadences, and building monitoring for data quality, accuracy, and freshness.
Proficiency with data visualization tools such as D3.js, Tableau, Streamlit, or React to turn complex data into actionable insights.
Strong interpersonal, communication, and collaboration skills.

Responsibilities

Design, build, and maintain end-to-end data pipelines that unify data from disparate sources into a trusted operational data model.
Develop and operate high-volume, real-time and batch data platforms for high-resolution time-series data using Python, modern orchestration, and streaming, with scalability and low latency.
Architect robust databases and storage for infrastructure, equipment, power, and energy data, and build reconciliation, validation, and monitoring logic to ensure data quality, accuracy, and freshness across the platform.
Build, train, and deploy time-series forecasting, optimization, and anomaly-detection models for power demand, energy consumption, cost, and capacity, turning operational data into predictions and optimization strategies that inform decisions.
Design and build backend APIs, such as FastAPI, Flask, or Django, to serve structured data and model outputs to dashboards, applications, and other platform tools.
Deliver interactive dashboards and tooling that turn power, energy, and cost data into actionable insights for design engineers, facilities, and leadership.
Scale data collection and pipelines across multiple sites and regions, including automated sensor synchronization and scheduled computations that feed large-scale real-time forecasting and optimization.
Partner with software and machine learning engineers to translate operational requirements into data architecture and modeling decisions, and define and improve engineering standards to keep systems production-grade.

Category	Construction & Facilities
Location	PALO ALTO, California
Requisition ID	280079
Type	Full-time

Job Description

As a Data Engineer on the Infrastructure DesignX team, you will play a critical role in building and scaling the data foundation that powers the Machine Learning Platform optimizing and automating infrastructure planning and construction across our national buildout. You will focus on building and scaling robust data pipelines and models that source, transform, and serve large-scale datasets enriched with real-world field context. This data powers the neural networks, forecasting, and optimization our engineers rely on. Delivering trustworthy, high-resolution data at scale and turning it into predictions and insights in the shortest amount of time is central to our mission. The Machine Learning Platform empowers our engineers to design, build, and manage infrastructure across the nation faster and more efficiently. As infrastructure buildouts, power demands, and construction costs grow more than ever, we're looking for an exceptional data engineer to drive scalability improvements, new capabilities, and the expansion of the platform's data and modeling infrastructure across sites and regions.

Job Responsibilities

Design, build, and maintain end-to-end data pipelines that unify data from many disparate sources into a single, trusted operational data model
Develop and operate high-volume, real-time and batch data platforms for high-resolution time-series data using Python, modern orchestration, and streaming with scalability and low latency
Architect robust databases and storage for infrastructure, equipment, power, and energy data, and build the reconciliation, validation, and monitoring logic that ensures data quality, accuracy, and freshness across the platform
Build, train, and deploy time-series forecasting, optimization, and anomaly-detection models for power demand, energy consumption, cost, and capacity - turning operational data into predictions and optimization strategies that drive real decisions
Design and build backend APIs (e.g., FastAPI, Flask, Django) that serve clean, structured data and model outputs to dashboards, applications, and other tools across the platform
Deliver world-class interactive dashboards and tooling that turn complex power, energy, and cost data into actionable insights for design engineers, facilities, and leadership
Scale data collection and pipelines across multiple sites and regions including automated sensors sync and scheduled computations that feed real-time forecasting and optimization at very large scale
Partner cross-functionally with software and ML engineers to translate operational requirements into data architecture and modeling decisions - while defining and improving engineering standards to keep everything production-grade

Job Requirements

Degree in computer science or equivalent, and 3+ years of professional experience as a data engineer or backend engineer building large-scale data platforms
Expert in Python and SQL building performant queries, data models, and pipelines at scale (e.g., PostgreSQL, MySQL, SQL Server, Data Lake)
Experience across the full data lifecycle - sourcing, ingestion, transformation, analytics, and visualization - with the ability to own projects end-to-end
Hands-on with data processing and orchestration frameworks (e.g., Spark, Airflow, dbt, Dagster) in production at scale
Production experience with real-time / distributed streaming systems (e.g., Kafka, Spark Streaming, Flink) handling high-volume, high-resolution time-series data
Proven experience building and deploying time-series forecasting models in production on high-resolution operational data
Hands-on experience formulating and solving mathematical optimization problems -mixed-integer linear programming and convex/non-convex optimization (e.g., using PuLP, Pyomo, Gurobi, CVXPY)
Experience integrating and reconciling data across multiple source systems with conflicting schemas or update cadences, and building monitoring for data quality, accuracy, and freshness
Proficiency with data visualization tools (e.g., D3.js, Tableau, Streamlit, React) to turn complex data into clear, actionable insights
Excellent interpersonal, communication, and collaboration skills

About the Company

Tesla

Makes electric cars, robotaxis and grid batteries

Tesla makes electric cars and batteries that store power for homes and utilities, and it develops self-driving software and humanoid robots. In 2023, its Model Y became the first electric vehicle to rank as the world's best-selling car. Buyers order the Model 3 sedan, Model Y SUV, or Cybertruck pickup directly from Tesla instead of through dealers, and they charge on its Supercharger fast-charging network. It also sells Megapack and Powerwall batteries, runs a robotaxi service, and charges drivers a monthly fee for self-driving software that still needs their supervision. Tesla was founded in 2003, with Elon Musk as its lead early investor and a recognized co-founder, and it is based in Austin, Texas.

Company size: 10,001+
Company stage: IPO
Headquarters: Austin, TX
Founded: 2003

Recently Posted Jobs

Explore more opportunities at Tesla

See more jobs