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# Data Engineer

- **Company:** Gridmatic Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Salary:** $180,000.0 - $235,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Business Logic, Data Architecture, Data Cleansing, Data Infrastructure, Data Warehousing, Relational Databases, Python (Programming Language), Standard Sql, Scripting, Data Layers
- **Published:** August 19, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-engineer-san-francisco-ca--8e15e954-43ec-493a-bd30-dec11c4e937d

## About the Role

* 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, Cross-Functional, Data Cleaning, Data Warehousing, Diving, Fundraising Program, Machine Tool, Non-Profit Funding, Profit & Loss, Python Programming/Scripting Language, Relational Databases (RDBMS), Revenue Analysis, Startup, Supplier Optimization, Team Player

## Description

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.


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