Electricity grids are undergoing the most significant transformation in a century. The shift to renewables, the proliferation of rooftop solar, batteries, and EVs, and the increasing complexity of distribution networks are forcing utilities to operate their grids in fundamentally new ways.
Gridsight builds the analytics platform that makes this possible. We're already embedded with most of Australia's major distribution networks and have contracts with some of the largest investor-owned utilities in the United States. We're well-funded, growing quickly, and positioned at the centre of a global shift toward dynamic grid management.
The Role
We're hiring a Staff-level Data Engineer to build and evolve the data layer that powers our grid analytics products.
This is a hands-on data engineering role. You'll design pipelines, write transformations, and ship data that downstream consumers — data scientists, product engineers, and analysts — depend on daily. What makes this a Staff-level role is what you bring on top of that: modelling discipline. If you're the kind of engineer who designs clear data artefacts with correct grain, clean layering, and well-defined contracts — so that the complexity of a domain is expressed in simple models, not buried in the code, and downstream teams can build on it confidently — this is your role.
What You'll Do
- Design, build, and maintain scalable data pipelines that transform and serve data for analytics and platform features
- Bring modelling discipline to the data layer — designing dimensional models (Kimball) and layered transformation architectures that decompose complexity into modular, well-abstracted layers
- Ensure data quality, reliability, and observability across the pipelines and models you own
- Collaborate with Data Science, Product Management,Software Engineering and Design to understand domain requirements and translate them into robust data structures
- Establish data engineering standards — modelling conventions, testing practices, documentation, and transformation design patterns
- Identify and address data technical debt, particularly where poor abstraction is creating complexity downstream
- Mentor other engineers on data modelling, pipeline design, and transformation architecture
What You'll Bring
- 5+ years of data engineering experience with demonstrated impact at Staff or Senior level
- Strong experience building and maintaining production data pipelines at scale
- Strong data modelling fundamentals — dimensional modelling (Kimball), relational theory, and a clear sense of when to apply different techniques
- Hands-on experience designing layered transformation architectures (staging, intermediate, mart patterns) in production
- Experience with dbt or similar transformation frameworks
- Solid software engineering fundamentals: version control, testing, code review, CI/CD
- Fluency with AI-assisted development tools and workflows
- Experience working closely with downstream consumers and designing data that serves their needs
- A track record of bringing structure and clarity to complex or messy data landscapes
What Would Set You Apart
- Experience in energy, utilities, or grid technology
- Experience building data products with defined consumers, SLAs, and quality contracts
- Time-series data or operational analytics experience
- Background in data mesh principles or domain-oriented data architecture
- Experience with modern data orchestration tools (Airflow, Dagster, Prefect)
- Cloud data infrastructure depth (AWS, GCP, or Azure)
What We Offer
- Competitive salary and equity package
- Remote-first, with head office in Sydney
- A talented team of engineers, data scientists, and power systems specialists working on hard problems that matter
