You're a data engineer who ships. You're comfortable making calls without a committee, you're already using AI as a daily accelerant, and you'd rather build something properly from the ground up than inherit someone else's half-finished model.

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The business

This is a well-established, profitable Australian business operating in roadside and emergency assistance - a sector where data quality and operational reliability genuinely matter. Dispatch decisions, contractor performance, job fulfilment, customer outcomes - it all runs on data, and right now that data infrastructure needs an engineer who wants to build it properly.

The data team is small and the Head of BI is hands-on and technically strong - you'll have a real peer to work with, not a layer of management to report through. Leadership is actively investing in modern tooling and has a genuine, top-down commitment to AI - this isn't a business that's still debating whether any of this is a good idea.

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The role

You'll be the second dedicated data specialist in the team, and your first six months have a clear focus: establish a solid dbt practice and build the modelling layer the business can rely on. Bronze, silver, gold - proper foundations, done right, that the rest of the team can build on.

Beyond that, you'll be working across the full data stack:

• Designing and building layered dbt models - star schema, data vault, or both - with real thought behind the trade-offs
• Building and maintaining ELT pipelines using Python and/or AWS Glue into Redshift
• Working with AWS RDS (Postgres primary, some MySQL legacy), S3, Glue, and orchestration tooling
• Contributing to CI/CD pipelines and version-controlled infrastructure, including Terraform
• Supporting the migration from QuickSight to Power BI, making sure the modelling layer serves BI consumption patterns well
• Engaging with three internal agentic bots already running in production - including tooling that spins up RDS replicas so AI can work safely against production-mirrored data

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What you bring

The hard requirement is dbt. Not "some exposure" - production-grade experience building layered warehouse models, with a genuine understanding of why the patterns exist. If you can't talk fluently about modelling decisions and trade-offs, this probably isn't the right fit.

Beyond that:

• Strong SQL - the kind where you think in sets and write transformations others can read and extend
• Modern data warehouse architecture experience - bronze/silver/gold or equivalent
• AWS data stack: S3, Redshift, Glue, and orchestration (Airflow or similar)
• CI/CD and version control discipline - tested pipelines, reviewed code, documented models
• Experience working in a team-based data environment, not just as a solo operator

Useful but not essential: Python for pipeline development, Terraform, Power BI experience, RDS familiarity.

AI fluency matters here. About 80% of code on this team is AI-assisted - that's not a target, it's just the reality of how they work. If you're already there, you'll feel at home immediately.

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The environment

This is a high-enthusiasm, get-stuff-done kind of place. The people who thrive here make decisions with the information they have, take ownership without being asked twice, and find energy in building rather than waiting for perfect conditions.

If you're the kind of engineer who looks at an immature data environment and sees an opportunity rather than a problem, that's exactly the instinct this team is looking for.

Years of experience matters less than the quality of your thinking and your appetite for the work.

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What's on offer

• Competitive salary + super, commensurate with experience
• Hybrid working - 3 days in the Sydney CBD office
• A technically engaged leadership team actively pushing the boundaries of AI in a data engineering context
• Real influence over architecture, standards, and how the data practice evolves
• A small, tight-knit team where what you build is visible and valued

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Sound like your kind of role? Apply via the link below.