Data Engineer

At Magentus, our goal is to create a healthier society through technology. We do that by delivering smart workflows that connect people, systems, and data - making care more intelligent, efficient, and accessible. Whether it’s supporting clinicians, streamlining operations, or improving patient outcomes, we’re here to make a real difference in healthcare through digital innovation.

About the Role

As a Data Engineer, you'll take ownership of the reliability, quality and governance of our internal data platform. You'll design, build and improve data pipelines, introduce observability and automation, and help create a trusted, self-service data environment that scales with the business.

This is a hands-on engineering role suited to someone who enjoys solving root-cause problems, improving systems, and delivering practical outcomes that make a measurable difference.

You'll work closely with product engineers, delivery teams and data specialists, helping establish the foundations of a growing data capability within a purpose-driven healthcare technology organisation.

What we're looking for:

You're a practical, hands-on engineer who enjoys improving systems and building solutions that last. You combine strong technical capability with a continuous improvement mindset and are comfortable working with both technical and non-technical stakeholders.

What’s in it for you:

• Real impact. The data you make trustworthy shapes decisions about products used by thousands of clinicians every day.
• Genuine scope. This is a capability being stood up, not a seat being backfilled. You will have real influence over how it is built.
• A mature engineering environment. You will work alongside established product engineering, architecture, security and platform functions, with the delivery practices and regulatory obligations of a global health technology group.
• Building with AI. We actively develop agentic coding harnesses to accelerate delivery, and this role is one of the places we expect to drive that forward for data work, not just adopt what others build.
• Flexibility that is real. Brisbane-anchored and hybrid, with genuine flexibility in where and how you work.
• Professional growth. We support ongoing learning, conference attendance and certification relevant to your discipline.
• A values led organisation. Our values - We Care, Make a Difference, Resilience, One Team, Constant Evolution, and Trust - describe how we work with each other and with our customers.

Brisbane preferred, with hybrid and flexible working arrangements. Strong candidates elsewhere in Australia will also be considered.

Ready to bring your energy and organisation to a team that cares?

What you'll own:

• Pipeline reliability. Data lands where it should, when it should, and you know before anyone else when it doesn't. Manual intervention becomes the exception, not the routine.
• Data quality and consistency. Validation, reconciliation and testing built into the pipeline rather than bolted on afterwards, so the numbers hold up under scrutiny.
• Observability and documentation. Monitoring, alerting and lineage that make it possible to diagnose an issue quickly, plus documentation of what exists, where it came from and how it should be used.
• Self-service enablement. Reducing the support load by giving analysts and product teams the access, tooling and documentation to answer their own questions.
• AI-driven uplift. You use agentic tooling, including Claude, as a normal part of how you build and fix, and you actively help lift the rest of the data function to work the same wa

You’ll bring:

• 3-5+ years' experience in Data Engineering or a related software engineering role with significant data responsibility.
• Strong SQL and Python skills, with experience building and supporting production data pipelines.
• Hands-on experience working with Databricks in a production environment.
• Experience delivering end-to-end data integrations from source systems through to reporting or analytics platforms.
• Knowledge of data quality, testing, validation and reconciliation practices.
• Experience using GitHub, CI/CD pipelines and version-controlled development workflows.
• Experience using AI-assisted development tools to improve the way you design, build or troubleshoot solutions.
• Strong communication skills and a proactive, problem-solving approach.

Our Tech Stack

• Data platform: Databricks, SQL, Python
• Cloud and infrastructure: AWS, infrastructure as code
• Engineering practice: GitHub, CI/CD pipelines, automated testing, observability tooling
• Broader environment: Ruby on Rails, TypeScript and Node.js, React, PostgreSQL