Senior/Staff Data Engineer

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.

We're hiring a Senior to 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.

• Design, build, and maintain scalable data pipelines that transform and serve data for analytics and platform features
• 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 and systems
• Establish data engineering standards — modelling conventions, testing practices, documentation, and observability
• 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

• 5+ years of data engineering experience with demonstrated impact at Senior or Staff level
• Strong experience building and maintaining production data pipelines at scale
• Hands-on experience designing layered transformation architectures (staging, intermediate, mart patterns) in production
• Deep knowledge of modern data stack technologies (orchestration, transformation, storage, and streaming) and the Data Engineering lifecycle
• Solid data modelling fundamentals — dimensional modelling (Kimball), relational theory, and a clear sense of when to apply different techniques
• 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

• 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 distributed systems or stream processing
• Experience with modern data orchestration tools (Airflow, Dagster, Prefect)
• Cloud data infrastructure depth (AWS, GCP, or Azure)

• 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