Analytics Engineer II, Product Development

Rivian•Palo Alto, CA

About The Position

We are looking for an Analytics Engineer who will support our Product Development organization and cross-functional partners by architecting our data foundations. As part of the Product Development AI and Data Science team, you will be the bridge between source charger and vehicle telemetry and the high-stakes decisions that power our Charging, Battery, and many additional partner teams. Our mission is to bring clarity, rigor, and scale to the complex decisions required to build the world’s most adventurous electric vehicles. You will spend your time building the source of truth for critical engineering and business data—from low level vehicle telemetry to the real-world performance of our charging network. You are building the reliable data infrastructure that allows Rivian to scale. The ideal candidate must have an ability to ingest business and engineering needs and drive results with their data-based insights. They are adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

Requirements

Bachelor’s, master's, or PhD in Computer Science, Electrical Engineering, Mechanical Engineering, Materials Science, Physics, Mathematics, or another quantitative field
0-4 years of experience building production data pipelines and developing AI/ML solutions
High level of programming proficiency in Python and SQL. You should be comfortable writing clean, modular code and querying complex relational databases.
Analytics Engineering Mindset: Experience with and a strong desire to master dbt (data build tool) and Databricks/Spark environments.
Software Fundamentals: Demonstrated experience with version control (Git) and an understanding of how to move a model from a notebook to a stable, reproducible environment.
Domain Context: Prior experience or interest in physical engineering systems (Hardware, IoT, or Telemetry).
Strong problem-solving skills with an emphasis on product development
Excellent written and verbal communication skills for coordinating across teams and leading cross-functional efforts.
Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
A drive to learn and master new technologies and techniques

Nice To Haves

UI/Visualization: Experience building user-facing applications using tools like Plotly Dash, Streamlit, and Hex
DevOps for Data: Familiarity with Linux environments and CI/CD workflows for data pipelines.
Advanced Stats: Knowledge of statistical concepts (regression, distributions) to help validate the accuracy of the models you support.
Exposure to or hands-on experience with LLM application concepts such as retrieval, grounding, prompt/agent design, function/tool use, evaluation, safety/guardrails, and cost/latency optimization.
Passion: An obsession with electric vehicles, renewable energy, and the future of sustainable transportation.

Responsibilities

Architect the Source of Truth: Develop and maintain production-grade data models in Databricks and dbt that unify high-velocity vehicle telemetry and charging session data.
Build reliable, fast, and dynamic data tools, pipelines, and automated workflows that scale with our growing fleet and charging network.
High-Fidelity Reporting: Design and deploy clear, effective dashboards and apps in tools like Plotly Dash that adhere to strict performance SLAs and design quality standards for a wide range of stakeholders including executive leadership as well as external partners
Telemetry Insights: Extract and contextualize complex data from telemetry and business systems to identify performance bottlenecks and drive technical improvements in a range of outcomes across product development and business strategy.
Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Engineering Rigor: Implement software development best practices – including version control (Git), unit testing, CI/CD, and data quality monitoring – into the data lifecycle to ensure our reporting is as reliable as the vehicles we build.