The role

Gridmatic’s business is complex, and we need to build a robust data layer to be able to answer core questions around profit and loss and how to best optimize our renewable supply.

We’re looking for a startup-minded data engineer who can wear a lot of hats, work with multiple teams, and build the data platform needed to support answering these questions.

What you might work on:

• Revenue analytics. Building a unified set of financial data across trading, battery storage, and energy sales, allowing us to have a clear real-time view of revenue and PnL - an essential part of being an energy supplier.
• Energy supply analytics. Building tooling and infra so we can grow our renewable supply, e.g. tooling to easily simulate additional hypothetical renewable resources.
• Ownership of data ingest and transformation. For both revenue and supply, owning the data platform and building new ingest pipelines in Flyte and Python, or new models in DBT.

You might be a good fit if you:

• Are interested in diving into the domain of energy and financial data (not just data infrastructure) and grappling with complex business logic and messy data
• Have strong production Python and SQL skills
• Have a strong understanding of how to store and query data efficiently in relational databases and data warehouses
• Have production DBT skills, and are adept at clean data architecture
• Have used data orchestration tools like Flyte, Airflow, Prefect, or Dagster

This role requires candidates to adhere to our hybrid policy, 3 days a week in office with at least 1 day in our Cupertino office.