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Data Engineer

University of New South Wales · Sydney, Sydney Region · Full time · Hybrid

At a glance

Data Engineer at University of New South Wales, working with Python, SQL and Azure.

Location
Sydney, Sydney Region
Work mode
Hybrid
Job type
Full-time
Seniority
Junior
Technologies
Python, SQL, Azure, Git / CI/CD, Spark / Big Data
Visa sponsorship
Mentioned in the ad

Key requirements

  • Design, build, test and maintain secure, scalable data pipelines and models aligned with business requirements.
  • Apply advanced data engineering techniques to create curated, reusable data structures for enterprise reporting and analytics.
  • Resolve complex data quality, integration and performance issues using robust solution architecture.
  • Lead technical work packages from scope and testing through deployment and production issue resolution.
  • Provide consultative advice on data modelling, platform capabilities and technical constraints to EDO colleagues and UNSW stakeholders.

Hirepins insights

  • Python is asked for in 8% of the 24 live IT jobs in Australia on Hirepins.
  • 1 other Data Engineer role is open in Australia on Hirepins right now.

About this role

The Data Engineer will work in UNSW's Enterprise Data Office to design, build and maintain the Enterprise Data Intelligence Platform, focusing on data pipelines, modelling and integration. The role suits experienced data engineers with strong SQL, Python/PySpark and Azure skills who can work independently and advise stakeholders.

Key details

  • Location: Sydney, Sydney Region
  • Work mode: Hybrid
  • Contract: Full time, 35 hours per week, continuing position
  • Pay: Level 7– from $118,483 + 17% superannuation
  • Level: Senior
  • Visa sponsorship: The ad says sponsorship is not available

What you'll do

  • Design, build, test and maintain secure, scalable data pipelines and models aligned with business requirements.
  • Apply advanced data engineering techniques to create curated, reusable data structures for enterprise reporting and analytics.
  • Resolve complex data quality, integration and performance issues using robust solution architecture.
  • Lead technical work packages from scope and testing through deployment and production issue resolution.
  • Provide consultative advice on data modelling, platform capabilities and technical constraints to EDO colleagues and UNSW stakeholders.
  • Partner with stakeholders and delivery partners to capture requirements, balance interests and improve technical standards through peer reviews and knowledge sharing.

What you'll need

Must have

  • Tertiary qualifications in IT or equivalent experience in complex enterprise data engineering
  • Practical expertise building data engineering solutions using SQL, Python/PySpark, Azure Data Factory, Databricks and API/ETL/ELT patterns
  • Proven experience in data modelling and lakehouse architecture, including Kimball methodology and dimensional modelling
  • Strong analytical skills to resolve complex technical issues and deliver fit‑for‑purpose solutions
  • Excellent communication, stakeholder management and DevOps capabilities (CI/CD, Azure DevOps, Git, Unity Catalog) in Agile environments
  • Sound knowledge of data governance, security and Higher Education reporting frameworks (TCSI, TEQSA, QILT)

Nice to have

  • Desirable exposure to Microsoft Azure and Databricks

Tech stack

SQL, Python/PySpark, Azure Data Factory, Databricks, API/ETL/ELT, Kimball methodology, dimensional modelling, Azure DevOps, Git, Unity Catalog

What's on offer

  • Flexible Working Options (work from home, flexible hours etc)
  • Additional 3 days leave during December festive period
  • Career development opportunities
  • Up to 50% discount on UNSW courses
  • Flexible 17% Superannuation contributions, additional leave-loading payments and salary sacrifice
  • Discounts and entitlements (retail, education, fitness passport)

How to stand out for this role

Likely interview topics

  • Designing scalable data pipelines – discuss past projects, tools used and how you ensured security and performance.
  • Data modelling and lakehouse architecture – explain your experience with Kimball methodology, dimensional modelling and optimisation.
  • Resolving data quality and integration issues – describe troubleshooting approaches and example solutions.
  • Stakeholder communication and consultative advice – illustrate how you translate technical constraints into business language.
  • DevOps and CI/CD in Azure – talk about pipelines you built with Azure DevOps, Git and Unity Catalog.

Keywords to mirror in your CV

Data Engineer, SQL, Python/PySpark, Azure Data Factory, Databricks, Lakehouse architecture, Kimball methodology, Dimensional modelling, Azure DevOps, CI/CD

What to show

  • A Git‑hosted project showing an end‑to‑end Azure Data Factory pipeline that ingests, transforms and loads data into a Delta Lake using PySpark.
  • A documented case study of a data modelling solution following Kimball dimensional design, including performance benchmarks and governance considerations.

How to apply

Use the "Apply on company website" button to continue to University of New South Wales' careers page and submit your application there.

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