How to Hire a Data Engineer (in Perth)
The data engineering landscape has transformed dramatically in recent years.
10 years ago the role title didn't exist!
There were only Database Administrators, armed with a weapon of mass destruction: SQL.
Now the Modern Data Engineer have an weapons including Python, YAML and SQL at their disposal with air support from Databricks, Snowflake and Azure Data Factory and more.
But how to you find one of these elusive Data Engineers in Perth? We're about to find out.
What We're Up Against
In Perth, key challenges include a limited local talent pool, competition from east coast markets and the need to evaluate both traditional programming/ETL skills & cloud capabilities.
Perth hiring challenges:
Shortage of Data Engineers with both AWS/Azure and traditional data warehouse experience
Identifying the difference between traditional programmer Data Engineers and low-code ones
Need for engineers who can bridge legacy systems with modern cloud architecture
High competition for senior-level talent with mining sector domain knowledge
Growing gap between salary expectations and budgets
1. First We Must Get Our Definitions Correct
The data industry moves fast, so the role definition of Data Engineer varies and there is no agreed upon definition. In general, these are some typical responsibilities:
Building and maintaining scalable data pipelines
Implementing data governance and security measures
Creating efficient data storage solutions
Making source data available for business consumption
Supporting ML/AI initiatives
Enabling self-service analytics
Before getting started in hiring, you must get some processes in place so you can identify the right person. We use the WHO Method for hiring A Players - this breaks hiring into four key parts:
The Who Method; The A Method for Hiring
Beware, not every Data Engineer holds the same job title -> common role variations:
Data Platform Engineer
Data Infrastructure Engineer
ETL Developer
Big Data Engineer
Cloud Data Engineer
Data Analyst (with Data Engineering duties)
Typical Team Structure Position:
Reports to: Head of Data/Engineering Manager
Collaborates with: Data Scientists, Analysts, ML Engineers, Business Users
Often leads: Junior Engineers, Data Integration Specialists
2. What makes up a typical Data Engineer?
Not every Data Engineer is created in same. There is a clear two-party system that has established itself;
Traditional Programmer Data Engineers
Low-Code GUI Data Engineers
Both have their own advantages and benefit; one has stronger programming foundations while the other is more up to date with modern data ETL tools and technologies (and sometimes you can find a unicorn with both).
Must-Have Technical Competencies:
SQL (advanced level)
Python
ETL/ELT pipeline development
Data warehousing concepts
Version control
Additional Skills:
Terraform/Infrastructure as Code
Apache Spark
Real-time processing (Kafka/Kinesis)
dbt
Docker/Kubernetes
Data modeling
Perth-Specific Tools:
Snowflake (growing adoption)
Azure Data Factory/Synapse (most popular stack)
AWS (used in mining and some niche areas - less common)
Databricks (increasing adoption)
Fabric (just starting to get traction in government and NFP)
Certifications:
Microsoft Certified: Azure Data Engineer Associate
Databricks Certified Data Engineer Professional
Snowflake SnowPro Core Certification
AWS Certified Data Engineer
Popular Data Engineering Stack
3. Perth Market Insights
Current Market State:
High demand across mining, finance, insurance and education sectors
LinkedIn Talent Insights indicates 442 Data Engineers on LinkedIn (January 2025)
Battle for Snowflake and Databricks experience which is hard to come by
Azure dominates Perth from a market share perspective which creates more demand
Salary Ranges (2025):
Junior (1-3 years): $90,000 - $120,000
Mid-level (3-5 years): $120,000 - $140,000
Senior (5+ years): $140,000 - $160,000
Lead/Principal: $160,000 - $200,000
Contract Rates:
Mid-level: $700 - $900/day
Senior: $900 - $1,200/day
Lead/Principal: $1,200 - $1,400/day
Common Career Paths:
ETL Developer → Data Engineer
Software Engineer → Data Engineer
Database Administrator → Data Engineer
Data Analyst → Data Engineer
Which company hires the most Data Engineers in Perth? LinkedIn reports Fortescue.
Top Five Companies with individuals who list 'Data Engineer' as their job on LinkedIn. Percentage is YoY.
4. Interview Process Design
Different companies hire different ways depending on their culture and size. One process I recommend depending on organisational size and needs is below:
1. Screening interview: This is a quick video or call to see if the person is a basic fit.
This is your first chance to weed out the non-starters. It’s a quick 15–30 minute call designed to assess if the candidate meets the basic requirements of the role. Think of it as speed dating for Data Engineers—confirm their experience aligns with your must-have skills (e.g. Python, SQL, AWS) and clarify their interest in the position. Questions like;
“Can you briefly walk me through your experience with building data pipelines?”
“Which cloud platforms have you worked with, and what types of projects were they for?”
“What attracted you to this role and our company?”
“Are you currently working, and when would you be available to start?”
“What’s your ideal work environment—remote, hybrid, or in-office?”
2. Technical interview: Now it’s time to dig deeper. Let's get technical.
Time to dig deeper. This round tests their technical mettle and problem-solving skills. Depending on your setup, you can include a mix of live coding, a take-home task or a system design discussion. Pro tip: Focus on their approach and thought process as much as their final solution.
Technical Test Recommendations:
Practical pipeline development task
System design whiteboarding session
Code review exercise
SQL optimisation challenge
Live bug solving with provided code
Sample Technical Questions:
"How would you design a real-time data pipeline for equipment sensor data?"
"Explain your approach to incrementally loading large datasets"
"How do you handle sensitive data in ETL processes?"
3. Cultural/Team interview: Final formal interview round and a meet the team.
The final formal round lets your team meet the candidate and gauge how well they’ll fit within your company culture. This interview isn’t about technical chops—it’s about communication, collaboration, and attitude. Have them discuss their role in past projects, share how they’ve handled challenging team dynamics, or explain how they approach knowledge sharing. Let team members ask questions to get a feel for working together. Often works best when there are several neck and neck individuals.
4. Reference interview: Don’t skip this! Contact previous employers to verify their work history.
Don’t skip this step! References can be a goldmine of insights about your candidate. Reach out to previous employers or colleagues and ask targeted questions like:
“Can you describe the candidate’s technical strengths and weaknesses?”
"I don't like surprises, what's something you can tell me about this person that would help me not be surprised?"
“If you had parting words of wisdom for this person so they could grow, what would they be”
A quick 15-minute reference call can validate your decision and save you headaches down the line.
Red Flags:
No experience with version control
Unable to explain data modeling concepts
Lack of testing methodology
No experience with cloud platforms
GitHub doesn't light up like a Christmas tree
Sometimes people put cultural first and technical second or vice versa; it's usually dependent on the company and hiring manager.
Stuck for questions? Here is a GitHub repo with over 1000 data engineering questions. Use with caution as an employer as their probably what applicants practice on too!
5. Attracting the Best Talent
It's the question on everyone's mind: "Where do all the good Data Engineers hang out?" 😂Unfortunately, they’re not lurking in the corner of your local pub (although some I know don't mind Gramercy Bar & Kitchen).
Here's where to focus your efforts:
LinkedIn: Still the go-to for professionals. As mentioned, there are 400+ listed Data Engineers on LinkedIn you can connect and reach out to directly (not all may be receptive to you joining).
Meetup Events: Perth has a growing data community. Look out for events like “Perth Data Engineering Meetup” or tech-specific meetups focusing on AWS or Snowflake.
Referral Networks: Referrals are undoubtedly the best hiring pathway if you can get someone because it lowers risk and increase team collaboration.
Discord & Slack: There are a few online groups that are invite only. DM me for more information.
Recruiters: Recruiters have their own network, database and systematic approaches to hiring so can add not only active talent but passive "not applying for jobs" talent as well.
Job Ads: Popular platforms are Seek and LinkedIn and ads will attract a change of actively applying individuals.
Pro Tip: Don’t ignore expats or interstate professionals willing to move for a Perth lifestyle. The beaches are a hard sell to resist. Interstate is often a easier conversation than the VISA required with international.
6. Common Hiring Mistakes
Typical Pitfalls:
Overemphasising specific tools over fundamental concepts and team fit
Ignoring soft skills and communication abilities
Not assessing problem-solving methodology
Missing cultural fit evaluation
Not raising working from home policy and salary range early
Warning Signs:
Inability to explain previous projects clearly
Lack of questions about data governance
No interest in understanding business context
Poor version control practices
7. Wrapping It Up
Hiring a Data Engineer in Perth is like prospecting for gold—time-consuming, occasionally frustrating, but ultimately rewarding. With the right strategies, you’ll unearth someone who will be the plumber to your data (poor analogy).
In short:
Be specific about the skills you need and offer room for growth.
Leverage multiple channels to identify the right talent
Invest in multiple interviews to ensure long-term success.
If all else fails, give us a call. We’ve got a knack for turning hiring data folk 🤓
