Data Platform Engineer

About the role

Hey there! Thanks for stopping by. We’re Airtasker, the tech company that connects people who need to get things done with those who have the skills to do it.

Our mission is to empower people to realise the full value of their skills. This doesn’t just apply to those who use our product. It’s also for our whole team of go-getters who bring the product to life and deliver impact to our community on a daily basis. Because we believe work (and life) is always better when you truly know and appreciate your strengths and skills.

We're looking for a Data Platform Engineer to help build and operate the data infrastructure powering reporting, analytics, and AI products across Airtasker.

This is a hands-on engineering role where your work has a direct line to business outcomes. You'll own meaningful pieces of the platform - clean dbt models other teams rely on, reliable Dagster pipelines that recover gracefully, and ingestion connectors that just work. You'll grow your scope across cloud infrastructure, orchestration, governance, and the AI-enabled pipelines reshaping what data teams can deliver.

This is a Sydney based role with 2 days per week in the office.

WHAT YOU'LL DO:

- Build and maintain core data pipelines: Design and operate reliable, business-ready data models and transformation pipelines using dbt, SQL, and Python to support analytics and downstream use cases.

- Scale our data platform: Contribute to the design and operation of AWS-based data infrastructure (including Terraform), balancing scalability, reliability, and cost effectiveness.

- Strengthen orchestration and reliability: Help build and evolve our Dagster-based orchestration platform, improving monitoring, resilience, and operational maturity.

- Enable self-service analytics: Deliver high-quality foundational datasets and tooling that improve data accessibility and accelerate decision-making across the business.

- Improve data quality and trust: Apply software engineering best practices (CI/CD, testing, documentation) to ensure data products are robust, auditable, and trusted.

- Collaborate and automate: Work closely with analysts, data scientists, and engineers, and use Python to automate workflows and continuously improve the data platform, including supporting production ML use cases.

WE'D LOVE TO HEAR FROM YOU IF YOU TICK SOME OF THESE BOXES:

- A solid understanding of the data lifecycle, from ingestion through to analysis and consumption.

- Strong SQL skills for data modelling, transformation, and analysis.

- Hands-on experience using dbt in a professional or personal project setting.

- Familiarity with software engineering principles, including version control, testing, and CI/CD.

- Experience using Python for scripting, automation, or data manipulation.

- The ability to understand business problems and translate them into clear data models and pipelines.