Specialisation — Data

Data Engineers

Data Engineers who make AI possible. AI systems are only as strong as the data feeding them.

The Role

What Data Engineers Do

Data Engineers build the pipelines, platforms and quality controls that allow ML and AI teams to work with reliable data. For AI-heavy environments, we look for more than SQL and dashboards — we look for data engineering that supports production ML and analytics at scale.

Roles We Place
Data Engineer
Senior Data Engineer
Lead Data Engineer
Analytics Engineer
Data Platform Engineer
Streaming Data Engineer
Technical Skills

Technologies We Screen For

We technically vet every Data candidate against these core skills.

Python SQL Spark dbt Airflow Kafka Flink Snowflake BigQuery Databricks Redshift AWS/GCP/Azure Terraform Data Modelling Data Quality Data Governance
Market Insight

What the Market Looks Like

Data engineering is the backbone of every AI initiative. Australian companies are investing heavily in modern data stacks (dbt, Snowflake, Databricks), creating strong demand for engineers who can build production-grade pipelines. The role has evolved significantly from traditional ETL — modern data engineers are closer to software engineers.

FAQ

Common Questions About Hiring Data Engineers

How do Data Engineers support AI/ML teams?
Data Engineers build the pipelines that deliver clean, reliable data to ML models. They create feature pipelines, manage data quality, build real-time streaming systems for online inference, and ensure data governance. Without strong data engineering, ML teams spend 80% of their time cleaning data instead of building models.
What modern data stack skills should I look for?
The modern data stack typically includes: dbt for transformation, Snowflake or BigQuery for warehousing, Airflow or Prefect for orchestration, and Spark or Flink for large-scale processing. We screen for hands-on experience with these tools, not just awareness.
Do you recruit Analytics Engineers as well?
Yes. Analytics Engineers sit between data engineering and analytics — they transform data using dbt, build semantic layers, and create reliable datasets for business intelligence. We place them alongside traditional data engineers.

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