Data Engineer (m/w/d)

Gridmatic is a high-growth startup and a new kind of energy company, delivering affordable, clean power by optimizing renewable energy and grid-scale batteries. With offices in the Bay Area and Houston, we bring together Silicon Valley-style innovation with deep, hands-on expertise in real-world power markets and energy retail.

As solar and wind become the fastest-growing sources of electricity, variability from weather and grid conditions makes energy prices more volatile. Gridmatic tackles this challenge with industry-leading forecasting and optimization-and gives our team the opportunity to work on problems that truly matter. Forecasting and trading energy are the foundation of what we do. We ingest large-scale data-weather, prices, load, and grid conditions-to build probabilistic machine learning forecasts that drive real operational decisions. Our work directly determines when power is bought, stored, or deployed, turning uncertainty into value for customers and the grid.

Our impact is measurable. Gridmatic is the most profitable participant in ERCOT's wholesale market and operates the top-performing battery asset in CAISO. Profitable without venture capital, we offer a collaborative, low-ego environment where rigorous thinking, autonomy, and continuous learning are core to how we work.

The role

We're looking to hire our first data engineer at Gridmatic! Looking for a startup-minded engineer who works closely with our ML and optimization teams to ingest and transform the data critical to all the work we do.

We use a lot of interesting, real-world data - large-scale weather forecasts, timeseries data from the grid and energy markets, and telemetry from physical batteries. We're looking for someone who'd be able to take ownership of this area to make sure the data is ingested and transformed reliably.

What you might work on

• Owning the ingestion and transformation of datasets needed for mission-critical operations like machine learning, renewable energy supply, and battery storage.
• Designing data models and choosing good data persistence strategies around large volumes of energy and weather timeseries data.
• Creating data products using DBT and dashboards/visualizations to help us make key business decisions.
• Helping inform our data architecture, and best practices around storing and using data.

What we're looking for

• A strong data engineer who has worked on large-scale production data pipelines, and can take ownership of critical datasets.
• Has worked with large-scale data, and makes good choices on data storage and schema design (relational databases, data warehouses, object storage, timeseries data).
• Has worked at a startup or similar environment, and works well with ambiguity and having a lot of scope/responsibility.
• Has strong software engineering skills. Being able to write easy-to-extend and well-tested code.
• Has experience with data processing tools like DBT, spark, kafka, flink, beam, dataflow, etc.

Our stack includes: Python, GCP, Kubernetes, Terraform, Flyte, Temporal, React/NextJS, Postgres, BigQuery, DBT.

$180,000 - $235,000 a year

Join our team and make a difference! Click below or email us at [email protected].

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.