Job Description

Mission

Skip is building the fastest, most intelligent home loan platform in Australia - and we increasingly run on data: credit performance, acquisition economics, settlements and servicing, and the numbers our board, warehouse funders and regulators rely on. This is our first dedicated data hire . The job is to turn raw, ingested data from a wide array of sources into trusted, decision-ready answers - and to build the modelling discipline so those numbers stay trustworthy as we scale many times over.

About the role

We’re hiring the person who, faced with a recent business question, instinctively asks: how do we model this once, test it, and make it self-serve? - and then builds the clean, tested data models and shared definitions that make the answer obvious to everyone - whether querying and interpreting from a dashboard or from an agent chat. A founding generalist who is genuinely disciplined at modelling and a strong, business-literate communicator and the founding owner of a function we expect to grow - you may build and lead the data team that follows.

You will own three parts that most companies split across two or three people:

- The ingestion layer - the pipelines that land raw data from our source systems into Snowflake reliably and on time, so everything downstream is built on complete, fresh data.

- The model layer - the transformation, dimensional modelling, testing and documentation that turn raw Snowflake data into clean, reconciled, business-ready tables (dbt-style: staging → intermediate → marts).

- The analysis - partnering with all teams and Execs to answer the questions that actually move the business, and turning the numbers into decisions.





The thread connecting them is the same: make the trusted number the easy number. You measure your success not by how many dashboards are shipped, but by how many decisions get made confidently on numbers people trust.

The opportunity

We are a small, high-talent-density team scaling our business with technology. The companies that win this phase keep their teams small and their leverage high. Data is usually where that breaks: trust erodes, every team defines metrics differently, and answering a simple question turns into a week of reconciliation. The opportunity here is to prove the opposite - that one excellent person, building the right models and definitions early, can give the whole business trusted, self-serve answers while the team stays lean.

Key Responsibilities

- Own the data stack end to end - from ingesting raw source data into Snowflake through to the clean, reconciled, business-ready models the whole company relies on (the loan book, arrears, originations and the acquisition funnel).

- Model for trust: the right grain, keys and structure so marts are business-ready and reconcile across all reporting levels — with testing and version control as the default, not an afterthought.





- Establish and own metric definitions and a data dictionary - so numbers mean the same thing in credit, finance and the board pack.

- Build the core dashboards and reporting the business runs on - high-signal views people actually use - and own board, warehouse-facility and regulator-facing reporting so the numbers we report externally are trustworthy and defensible.

- Build toward a single source of truth and governed self-service, so the data function scales without becoming a bottleneck as request volume grows.

- Partner with Growth, Credit, Ops, Finance and Treasury - clarifying vague questions and ending with a concrete recommendation, not just a chart, and pushing back when a request is wrong.

- Use AI and technology to move fast on the mechanical work while owning the judgement and verification that keeps the numbers honest.

What you will bring

We’re looking for senior capability, expressed as autonomy and breadth more than years — but as a guide, this typically looks like 5+ years across analytics engineering, data analysis or BI, with the seniority to own outcomes end-to-end and set standards from scratch.

- You have owned a production data stack - the pipelines feeding the warehouse and a tested, documented, version-controlled model layer on top - not just “used dbt” once.

- Strong, demonstrable business-facing analysis - you have good instincts about which insights matter, can manage stakeholders and metric definitions, and reliably turn numbers into decisions.

- You think in definitions and trust - your instinct on seeing inconsistent numbers is to model and define them once, not to patch another query.

- You us

📌 Founding Data Lead (Greater Bendigo City)
🏢 Skip Loans
📍 Greater Bendigo City