Senior Data Engineer

About UsLoop 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 RoleYou'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.LeadingOwn the architecture from ingestion through to the models that power the product, and make the trade-off calls on cost, complexity, and time to valueSet engineering standards across the data team — code review, testing, documentation, definition of doneRun day-to-day delivery and prioritisation alongside the product and software engineering leadsMentor engineers through pairing and review, and help shape how the team hires as it growsBuildingDesign and maintain reusable in-house PySpark frameworks that standardise data engineering patterns across the platformArchitect production-grade ETL/ELT pipelines across AWS, with distributed processing in Python and PySpark on DatabricksBuild batch and near real-time ingestion integrating third-party clinical systems, healthcare APIs, and enterprise platformsDesign secure integration patterns (REST APIs, SFTP, event-driven ingestion, webhooks) that hold up to compliance and data integrity requirementsWork with the software team so the application backend and data layer integrate cleanlyImplement CI/CD with Git, plus infrastructure-as-code and environment management across AWSOptimise Spark jobs, cluster configuration, and storage for performance and costDesign robust data models, including dimensional and SaaS-oriented schemasBuild validation, monitoring, and alerting so pipeline failures are caught by us, not our customers
About You6+ years in data engineering, including production systems in a SaaS or product-led environmentExperience leading a small team or holding technical ownership of a platform — formal management experience welcome but not essentialAdvanced Python, SQL, and PySpark for large-scale distributed processingStrong hands-on AWS (S3, Lambda, RDS, Glue, IAM) and deep Databricks cluster experienceSolid grasp of lakehouse and cloud-native data architecture, and the judgement to know when the simpler option is the right oneExperience integrating third-party systems via APIs and secure data exchangeStrong data modelling and governance knowledgeAutomated 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 itDegree 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.