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 This is a hands-on build role. We have one engineering team made up of data engineers and software engineers — you’d join the data side, reporting to our Data Engineering Lead. You’ll own whole slices of the platform — end to end, from pulling data out of a third-party clinical system through to the models the product reads. Not tickets handed to you in isolation: you’ll be the person who knows a domain properly and is trusted to make the calls inside it. Sitting in the same team as the software engineers means you’ll work directly with the people building the product on top of your data, rather than throwing tables over a wall. It’s a small team, so the surface area is wide. You’ll write pipelines, shape data models, touch the infrastructure that runs them, and be on the hook when something doesn’t look right. What you’ll do • Build and maintain production ETL/ELT pipelines on AWS using Python and PySpark • Own ingestion from third-party clinical systems, healthcare APIs, and enterprise platforms — from the integration itself through to the tables people query • Model data in our lakehouse — incremental loads, slowly changing dimensions, and schema changes that don’t break anyone downstream • Handle sensitive data carefully, and help us keep de-identification correct as we add new sources • Write the tests, monitoring, and alerting that catch failures before customers do • Contribute to our shared in-house frameworks so patterns get reused rather than reinvented • Work in infrastructure-as-code and CI/CD alongside the rest of the team • Review other engineers’ work, and help lift how the team builds About You • 4+ years in data engineering building production pipelines • Engineering discipline - tests written with the code, work delivered in reviewable PRs, shared helpers extended rather than duplicated. • Lakehouse and table-format depth - production Iceberg or Delta: snapshots, schema evolution, copy-on-write vs merge-on-read, SCD1/SCD2. You know why an ad-hoc DELETE can break a downstream changelog reader while the job still reports success • Strong SQL, and solid PySpark for distributed processing • Hands-on AWS across compute, storage and orchestration. We run Spark on Glue; Databricks or EMR experience transfers fine • Comfortable owning something end to end, and asking when the requirements are unclear • Degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience Nice to have: healthcare, aged care, or other regulated data environments; multi-tenant SaaS data; infrastructure-as-code (we use Pulumi); familiarity with Australian privacy and health data obligations.