Senior Data Infrastructure Engineer

Key Skills

Aartificial intelligence, Apache Airflow, Apache Beam, Apache Flink, Apache Kafka, Data Engineering, Infrastructure as code, Machine learning techniques, SPARK Programming

Job Description

The Role:

We’re looking for an engineer to help lead the scaling and reliability of our data infrastructure, which is core to the ML work we do at Gridmatic.

Forecasting energy prices is challenging. We have very effective price forecasting models, but we’d like to go much further — scaling the amount of data we can use in our ML models by a factor of 10-100x by incorporating petabyte-scale weather data, increasing spatial granularity of our price forecasting, and more.

We’d also like someone who can tackle the challenge of scaling and improving reliability of our data platform. We deal with a lot of real-world problems when ingesting data from external sources — downtime, late-arriving data, changing schemas. Improving the reliability of our data pipelines will be critical to our ability to make an impact on the grid.

What we’re looking for:

• Experience building the infrastructure for large-scale data processing pipelines (both batch and streaming) using tools like Spark, Kafka, Apache Flink, and Apache Beam.
• Experience designing and implementing large-scale data storage systems (feature store, timeseries DBs) for ML use cases. Strong familiarity with relational databases, data warehouses, object storage, timeseries data, and being adept at DB schema design.
• Experience building data pipelines for external data sources that are observable, debuggable, and verifiably correct. Have dealt with challenges like data versioning, point-in-time correctness, and evolving schemas.
• Strong distributed systems and infrastructure skills. Comfortable scaling and debugging Kubernetes services, writing Terraform, and working with orchestration tools like Flyte, Airflow, or Temporal.
• Strong software engineering skills. Being able to write easy-to-extend and well-tested code.

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

What you might work on:

• Owning and scaling our data infrastructure by several orders of magnitude. This includes our data pipelines, distributed data processing, and data storage.
• Building a unified feature store for all our ML models.
• Efficient storing and loading hundreds of terabytes of weather data for use in AI-based weather models.
• Processing and storing predictions and evaluation metrics for large-scale forecasting models.

You might be great for this role if:

• You have 4+ years of experience building data infrastructure or data platforms
• You have experience with ML infrastructure and have worked at companies that use ML for core business functions
• You’re comfortable with ambiguity and a fast-moving environment, and have a bias for action
• You learn and pick up new skills quickly
• You’re motivated in making a real-world impact on climate and energy

$180,000 - $250,000 a year

You will also receive Stock Options (ISOs)

Taking care of you today:

• Continuing Education Opportunities
• Flexible PTO
• Medical, Dental and Vision plans with competitive employer contributions
• Pre-Tax commuter benefits
• $1500/year non profit donation matching program through Millie
• Home Office Stipend

Protecting your future for you and your family:

• 401K contribution match up to 4%
• Company-paid parental leave
• Company Paid Life Insurance
• Stock Option Loan Program

FAQ

What’s your policy on remote work?

We value the ability to work and collaborate in-person in our early stage as a startup, so Gridmatic has a hybrid policy that will ask you to work in our Cupertino office 3 days a week.

What is your interview process?

You’ll usually have a chat with the hiring manager or someone on the team about your background and experience. After that, depending on the role, you’ll either have a technical phone screen with an engineer, or work on a take-home project. If that goes well, we’ll have you on site in Cupertino for an interview panel with the team, which usually takes about 4 hours.