Senior Analytics Engineer

Data & AIposted 7w ago

About Redwood Materials

Redwood is localizing a global battery supply chain that seamlessly integrates recovery, reuse, and recycling — keeping critical minerals in circulation and driving the energy transition. Founded in 2017, we’re delivering low-cost and large-scale energy storage and producing battery materials in the U.S. for the first time, all from batteries we already have.

About Redwood Materials

Redwood Materials is building a circular supply chain for batteries. Founded in 2017, we recover, reuse, and recycle end-of-life batteries and manufacturing scrap, and use the recovered materials to produce battery components domestically and power energy storage installations. Our goal is to reduce the cost and environmental footprint of batteries by keeping critical minerals in circulation.

About the Role

Our central data and analytics team builds and operates the data pipelines that power reporting, analytics, and automation across Redwood. This is a hands-on engineering role: the majority of our work is code-based data pipelining and automated data analyses. You will spend most of your time on code-based analysis and building and maintaining production pipelines rather than working in a BI tool.

You will work directly with stakeholders in finance, supply chain, and operations to understand what they need, scope technical solutions, and deliver reliable, automated data products. We are looking for someone who can translate a business conversation into a well-engineered pipeline and stand behind it in production.

What You'll Do

Build, deploy, and maintain production-grade automated data pipelines using Python and SQL.
Perform one-off and automated data-driven analyses using financial and supply chain models and calculations.
Scope technical pipeline and automation solutions based on conversations with users and an understanding of the underlying business value drivers.
Develop and orchestrate pipelines using tools such as Dagster or Airflow, and manage transformations with dbt.
Apply CI/CD and sound software engineering practices (version control, testing, code review) to data workflows.
Deploy and run pipelines on cloud infrastructure, and help maintain the reliability of what we ship.
Partner with finance, supply chain, and operations stakeholders to deliver the metrics, datasets, and automations they rely on.
Own delivery of your work end to end, including scoping, prioritization, and follow-through.

What We're Looking For

3+ years building, deploying, and maintaining production data pipelines.
Minimum bachelor’s degree in quantitative or technical field such as Data Engineering, Computer Science, Applied Math, etc. Graduate degree preferred.
Domain expertise in financial metrics or in supply chain and operations.
Strong Python and SQL.
Experience with orchestration tools such as Dagster or Airflow, and with dbt.
Working knowledge of cloud infrastructure.
Familiarity with CI/CD and good coding practices.
Ability to scope technical data pipeline solutions and automations from stakeholder conversations and business context.

Nice to Have

Experience with AWS.
Domain expertise across both financial metrics and supply chain/operations.
Project management skills.
Experience with Starburst and OpenMetadata.

The position is full-time. Compensation will be commensurate with experience.

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