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 positive 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.
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📌 Senior Data Engineer (Melbourne)
🏢 Loop IQ
📍 Melbourne