Senior Data Engineer

About Us

Loop IQ is a purpose-built intelligence platform helping care organisations move beyond fragmented spreadsheets and manual reporting — delivering the accuracy, auditability, and confidence that regulated environments demand. Alongside the platform sits our strategic consulting wing, guiding organisations through implementation and optimisation so the technology delivers from day one.

Why Work with Loop IQ?

• Build something that matters — We’re solving a real problem in a sector that affects millions of Australians. The work you do here has a direct line to better outcomes in aged care.
• Grow with intention — Dedicated learning budgets, performance bonuses, and genuine wellbeing support.
• A culture worth showing up for — A small, high-trust team that values different perspectives, moves fast, and communicates openly. No politics, just good people doing meaningful work.
• Rare access, real impact — We operate at the intersection of health data, government, and enterprise, with relationships that are hard to find at this stage of a company.

About the Role

You’ll own the data platform end to end — architecture, standards, and roadmap — while leading a small team of data engineers (currently two, growing).

Expect roughly 70% build, 30% lead. You’ll still be in the code every week; you’ll also be the person the team looks to for direction, review, and unblocking.

Leading

• Own the architecture from ingestion through to the models that power the product, and make the trade-off calls on cost, complexity, and time to value
• Set engineering standards across the data team — code review, testing, documentation, definition of done
• Run day-to-day delivery and prioritisation alongside the product and software engineering leads
• Mentor engineers through pairing and review, and help shape how the team hires as it grows

Building

• Design and maintain reusable in-house PySpark frameworks that standardise data engineering patterns across the platform
• Architect production-grade ETL/ELT pipelines across AWS, with distributed processing in Python and PySpark on Databricks
• Build batch and near real-time ingestion integrating third-party clinical systems, healthcare APIs, and enterprise platforms
• Design secure integration patterns (REST APIs, SFTP, event-driven ingestion, webhooks) that hold up to compliance and data integrity requirements
• Work with the software team so the application backend and data layer integrate cleanly
• Implement CI/CD with Git, plus infrastructure-as-code and environment management across AWS
• Optimise Spark jobs, cluster configuration, and storage for performance and cost
• Design robust data models, including dimensional and SaaS-oriented schemas
• Build validation, monitoring, and alerting so pipeline failures are caught by us, not our customers

About You

• 6+ years in data engineering, including production systems in a SaaS or product-led environment
• Experience leading a small team or holding technical ownership of a platform — formal management experience welcome but not essential
• Advanced Python, SQL, and PySpark for large-scale distributed processing
• Strong hands-on AWS (S3, Lambda, RDS, Glue, IAM) and deep Databricks cluster experience
• Solid grasp of lakehouse and cloud-native data architecture, and the judgement to know when the simpler option is the right one
• Experience integrating third-party systems via APIs and secure data exchange
• Strong data modelling and governance knowledge
• Automated testing for data pipelines, and solid DevOps fundamentals (Git workflows, branching, CI/CD)
• Comfortable with ambiguity and limited process — you’ll be building some of it
• Degree in Computer Science, Engineering, Data Science, or a related technical field

Nice to have: healthcare, aged care, or other regulated data environments; standing up a data platform from an early stage; familiarity with Australian privacy and health data obligations.