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