Build at scale with real impact - Design and optimise data platforms that directly support clinical care, operational performance, and national health initiatives

Work at the forefront of AI and modern data platforms - Contribute to data pipelines supporting Machine Learning, Generative AI, and advanced analytics use cases

Shape the future of data at enterprise level - Influence architecture, governance, and how data is leveraged across one of Australia's largest health systems

What if your next role didn't just build pipelines - but powered the future of healthcare in Australia?

At St Vincent's Health Australia, data is more than infrastructure - it is the foundation for better patient outcomes, smarter decisions, and system-wide transformation.

We're looking for a Data Engineer who wants to step into a role where their work underpins advanced analytics, AI, and next-generation digital health services across a national organisation.

About the Program - Medicare Mental Health Check-In

As part of this opportunity, you will contribute to the Medicare Mental Health Check-In - a national digital mental health service delivered in partnership with the Australian Government.

This program brings together national partners across technology, clinical workforce, and digital innovation to deliver a new model of accessible, scalable mental health care.

Data engineering is critical to enabling this model - ensuring high-quality, secure, and scalable data pipelines that support real-time insights, performance monitoring, and AI-driven innovation.

About St Vincent's Health Australia

St Vincent's Health Australia is one of Australia's leading not-for-profit health and aged care providers, founded by the Sisters of Charity in 1857.

Today, our 30,000-strong workforce delivers care across public hospitals, private hospitals, aged care, home care and virtual care services across NSW, Victoria and Queensland.

Our vision is to deliver excellent and compassionate care within a better and fairer health and aged care system.

About Your New Role

As a Data Engineer, you will sit at the core of our data ecosystem - designing, building, and optimising data pipelines that enable analytics, reporting, and emerging AI capabilities.

You will work across clinical, operational, and technology teams to ensure data is accessible, reliable, and structured to support decision-making at scale.

This role goes beyond traditional data engineering - you will contribute to modern data platforms, AI readiness, and enterprise-wide data capability uplift.

This is a 12 month fixed term ,full-time role based at our Melbourne Group Office (hybrid), with national impact.

In this role , you will :

• Design, build and optimise scalable data pipelines (ETL/ELT) across modern data platforms
• Develop and maintain data warehouse and lakehouse environments (Azure, Databricks, Fabric)
• Implement software engineering best practices (CI/CD, testing, version control)
• Perform data modelling, architecture design, and optimisation
• Enable AI and ML readiness through high-quality, governed data pipelines
• Support development of vector databases and pipelines for Generative AI use cases (e.g. RAG)
• Ensure data security, governance, and compliance with healthcare standards
• Collaborate with data scientists, analysts, and business stakeholders
• Monitor, troubleshoot, and continuously improve data platforms and pipelines
• Contribute to documentation, standards, and uplift of data engineering capability

About You

You are a highly capable Data Engineer who thrives in complex environments and enjoys building robust, scalable data solutions.

You're confident working across technical and business teams, and bring a strong focus on quality, performance, and long-term data architecture.

Essential Experience

• 5+ years' experience in data engineering, data warehousing, or data architecture
• Strong experience with Azure data platforms (ADF, Databricks, Fabric)
• Proficiency in Python or Scala, alongside advanced SQL
• Experience designing ETL/ELT pipelines and modern data architectures
• Strong understanding of data modelling, relational databases, and warehouse design
• Experience with Power BI, SSAS, or similar analytics tools
• Knowledge of CI/CD, DevOps practices, and Infrastructure as Code

Desirable

• Experience in healthcare or regulated environments
• Exposure to Machine Learning, MLOps, or Generative AI (RAG, LLMs)
• Experience with containerisation (Docker, Kubernetes)
• Understanding of structured and unstructured data environments

Why Join St Vincent's?

This is more than a technical role - it's an opportunity to build the data foundations that power better care.

At St Vincent's, you will:

• Work on meaningful, high-impact data challenges
• Be part of a purpose-driven organisation grounded in Compassion, Integrity, Justice and Excellence
• Access salary packaging benefits to increase your take-home pay
• Work in a collaborative, forward-thinking data and technology environment
• Contribute to large-scale digital health and AI transformation initiatives

Ready to Apply?

If you're passionate about building data platforms that make a real difference, we'd love to hear from you.

Please apply with:

• An up-to-date CV
• A tailored cover letter
• Responses to the mandatory application questions below

Mandatory Application Questions

(Maximum 500 words per question)

1. Data Engineering at Scale

   Describe your experience designing and building data pipelines or data platforms in a complex environment. What technologies did you use and what impact did your work have?

2. Data Quality, Performance and Optimisation

   Provide an example where you improved data quality, performance, or reliability within a data platform. What approach did you take?

3. Stakeholder Collaboration

   Tell us about a time you worked with both technical and non-technical stakeholders to deliver a data solution. How did you ensure alignment and successful delivery?

Please note: Applications are reviewed as they are received. The role may close early if a suitable candidate is identified.

Important Information

Your employment is subject to pre-employment checks, including relevant immunisations.

Equal Opportunity

We welcome applications from people of all backgrounds, identities and abilities - including Aboriginal and Torres Strait Islander people, culturally and linguistically diverse communities, LGBTQIA+ communities and people with lived experience of mental health challenges. Inclusion is essential to our mission - and diversity reflects the community we serve.

• Design, build and optimise scalable data pipelines (ETL/ELT) across modern data platforms
• Develop and maintain data warehouse and lakehouse environments (Azure, Databricks, Fabric)
• Implement software engineering best practices (CI/CD, testing, version control)
• Perform data modelling, architecture design, and optimisation
• Enable AI and ML readiness through high-quality, governed data pipelines
• Support development of vector databases and pipelines for Generative AI use cases (e.g. RAG)
• Ensure data security, governance, and compliance with healthcare standards
• Collaborate with data scientists, analysts, and business stakeholders
• Monitor, troubleshoot, and continuously improve data platforms and pipelines
• Contribute to documentation, standards, and uplift of data engineering capability

• Pipelines
• Machine Learning
• Analysis
• Azure data platforms (ADF, Databricks, Fabric)
• Python or Scala
• Advanced SQL
• ETL/ELT pipelines
• Modern data architectures
• Data modelling
• Relational databases
• Warehouse design
• Power BI, SSAS, or similar analytics tools
• CI/CD, DevOps practices
• Infrastructure as Code
• Healthcare or regulated environments (Desirable)
• Machine Learning, MLOps, or Generative AI (RAG, LLMs) (Desirable)
• Containerisation (Docker, Kubernetes) (Desirable)
• Structured and unstructured data environments (Desirable)

• 5+ years' experience in data engineering, data warehousing, or data architecture
• Strong experience with Azure data platforms (ADF, Databricks, Fabric)
• Proficiency in Python or Scala, alongside advanced SQL
• Experience designing ETL/ELT pipelines and modern data architectures
• Strong understanding of data modelling, relational databases, and warehouse design
• Experience with Power BI, SSAS, or similar analytics tools
• Knowledge of CI/CD, DevOps practices, and Infrastructure as Code
• Experience in healthcare or regulated environments (Desirable)
• Exposure to Machine Learning, MLOps, or Generative AI (RAG, LLMs) (Desirable)
• Experience with containerisation (Docker, Kubernetes) (Desirable)
• Understanding of structured and unstructured data environments (Desirable)