Software Engineer, ML Data Reliability

The role

We’re looking to hire our first data software engineer at Gridmatic! Looking for a startup-minded eng 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 a hybrid software and data engineer who’d be able to take ownership of this area to both make sure the data is ingested and transformed reliably, and also be able to build the tooling/abstractions to make our pipelines better.

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 building 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 software engineer + data engineer hybrid 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.