SQL Developer — Payroll Calculation Engine

Role Overview WageSafe builds the payroll compliance engine that stands between our customers and wage underpayment. We process complex payroll scenarios across multiple award types, handling overtime tiers, minimum guarantees, shift continuity, allowances and superannuation calculations for thousands of workers. We are an AI-first engineering team. Our platform runs on a modern cloud data stack — SQL transformations over a cloud warehouse, orchestrated in Python, versioned in GitHub and deployed on AWS — and it is built by a small team using Claude and AI coding assistants as the default way of working, not an optional extra. As a SQL Developer, you'll build, test and maintain the transformation logic and pipelines that power the calculation engine, working within a cross-functional platform team alongside data engineers, product managers and QA specialists. Why This Role Matters Australian wage compliance is a legal-exposure problem disguised as a data problem. Modern awards interact in ways no spreadsheet survives — overtime tiers stacking against minimum guarantees, shifts that cross midnight, allowances that trigger on conditions three rules deep. Getting it wrong at scale doesn't produce a bug ticket; it produces a remediation program and a regulator. Every stage of our pipeline involves nuanced conditional business rules, temporal logic and multi-tenancy. The code you write determines what lands in people's bank accounts, so precision and a willingness to learn the domain deeply are essential. Key Responsibilities • Feature Implementation: Write and maintain the SQL and Python logic that handles business requirements and edge cases — shift continuity, overnight conditions, specialised allowances, award-specific overrides. • Correctness Validation: Reconcile calculated pay against historical runs and known-good baselines. Build the regression suites that pin behaviour across every award variation we support, so a change to one rule can't silently break another. • Auditability: Ensure every calculated cent has a traceable derivation — which rule fired, on which input, under which award version. Determinism and explainability are product requirements here, not nice-to-haves. • Performance: Pay runs have deadlines. Profile queries, read execution plans, and refactor CTEs, window functions and warehouse workloads so the batch lands on time and doesn't cost a fortune doing it. • Code Review Participation: Give and receive peer review on pull requests — checking correctness, performance and adherence to payroll business rules. Every change is reviewed and tested regardless of what wrote it; you own what you ship. • Deployment & Support: Deploy validated changes through our CI/CD pipeline to production on AWS, monitor execution, and run down calculation discrepancies when they surface. • Testing Collaboration: Work closely with QA to build and validate automated test suites that accurately capture business rules. • Continuous Learning: Build your knowledge of advanced SQL patterns, the payroll domain, and effective AI-assisted development — and share what works with the team. Key Technical Requirements Must-Have 2–4 years of professional SQL development in production data environments. Solid SQL skills: window functions, CTEs, JOIN strategy, and genuine comfort with temporal logic — intervals, overlaps, boundaries, timezone and daylight-saving edges. Our hardest bugs live here. Working Python. You don't need to be a framework expert; you do need to write Python that other people can read, test and rely on. AI-first working practice. You already build with Claude, Copilot or equivalent as a core part of how you work — and you have the judgement to know when to trust the output and when to verify it line by line. We'll ask you to walk us through both, including a decision an assistant made that you overrode and why. Git and GitHub as daily practice — branching, pull requests, meaningful review, CI checks. ETL fundamentals: experience building or maintaining data pipelines, with a working understanding of pipeline dependency, batch failure and recovery, and data warehouse concepts. Performance tuning exposure: you've read an execution plan and acted on it. A testing instinct — you reach for a test before you reach for a fix, and you've built or maintained automated suites over data transformations. Comfort with ambiguity. Award interpretation is genuinely unclear sometimes; you ask the question rather than guess and move on. Good communication skills and comfort working in an Agile/Scrum delivery model. Based in Sydney, NSW. Hybrid — three days on-site. Nice-to-Have Domain Experience: Exposure to payroll, HR or time & attendance systems — modern awards, EBAs, superannuation, shift-based pay structures. Cloud Data Platforms: Hands-on with Snowflake, Databricks, or another cloud warehouse or lakehouse. Transformation & Orchestration Tooling: dbt, Airflow, Dagster or similar. Advanced AI Practice: Agentic coding workflows, AI-assisted code review, MCP servers, or building internal tooling on top of LLMs. Engineering Practice: GitHub Actions or equivalent CI/CD, automated test frameworks, version-controlled schema management. Advanced SQL Patterns: JSON parsing in SQL, recursive CTEs, or metadata-driven schema validation. Software Engineering & Integration: REST APIs or .NET technologies (C#). Data Visualisation: Exposure to BI platforms such as Power BI or Tableau.