Data engineers are among the most sought-after technology professionals in Australia. Every organisation that wants better analytics or AI needs reliable data underneath it, and the people who build that foundation have plenty of choice. Here's how to hire well.
1. Define the role you actually need
"Data engineer" means different things in different teams. Before you write a job ad, decide which kind of engineer you need:
- Platform-focused. Designs and runs the data platform itself: architecture, security, infrastructure as code, cost and performance.
- Pipeline-focused. Builds and maintains ingestion and transformation pipelines from source systems, batch or streaming.
- Analytics engineering. Models clean, tested data for reporting and analysis, usually with SQL and dbt, close to the business.
Then describe what success looks like after six and twelve months. Outcomes attract better candidates than a list of tools.
2. Separate must-haves from nice-to-haves
Strong SQL, solid Python, data modelling and experience with at least one major cloud platform are genuine must-haves for most roles. A specific product, such as Databricks rather than Snowflake, usually isn't. Good engineers move between platforms quickly. Requiring an exact tool stack shrinks your pool and screens out people who would excel.
3. Look beyond the job boards
Many of the best data engineers aren't actively applying. They're busy delivering, and they hear about new roles through their network, through referrals or from a recruiter they trust. Combine advertising with referrals, community events and targeted search to reach them.
4. Assess real experience, not keywords
A CV tells you which tools someone has used. A good interview tells you how they think. Questions that work well:
- Walk me through a pipeline or platform you built end to end. What would you change now?
- How did you test and monitor it in production?
- Tell me about a time a pipeline failed. How did you find and fix the cause?
- How did you manage performance and cloud costs as data volumes grew?
- How would you model this business process for reporting?
If you use a technical exercise, keep it short and relevant, or run it as a paired session. Long unpaid take-home tasks put off the strongest candidates.
5. Move quickly and sell the role
Strong candidates are often off the market within weeks. Keep your process to two or three stages, give feedback within a couple of days, and use each conversation to sell the opportunity: the data challenges, the stack, the team and the impact the role will have.
6. Make an offer that lands
Benchmark the salary against the current market before you go out, not after a candidate declines. Flexibility, learning budgets, certifications and interesting work all matter to data engineers. Be ready for counter-offers, and keep in touch between offer and start date.
Permanent or contract?
If you need delivery now and capability later, a blend often works best. Our guide to choosing between contract and permanent hires explains when each makes sense.
Need help with a data engineering hire? See our data engineering recruitment service or talk to a specialist.