Founding Data Lead

Role overview

This is a founding data leadership role at a fast-scaling home loan platform in Australia, focused on building the company's first dedicated data function from scratch. The mission is to turn raw, ingested data into trusted, decision-ready numbers that the credit, growth, finance, operations, and executive teams rely on, while establishing the modelling discipline that keeps those numbers trustworthy as the business scales. The work combines analytics engineering, business-facing analysis, and stakeholder partnership at true founding-ownership scope.

Responsibilities

- Own the full data stack end-to-end: ingestion pipelines that land raw source data into the warehouse, transformation and dimensional modelling into business-ready tables, and the analysis layer that drives decisions - Model for trust with correct grain, keys, structure, testing, and version control as defaults, ensuring marts reconcile across all reporting levels - Establish and maintain canonical metric definitions and a data dictionary so numbers mean the same thing across credit, finance, growth, and the board pack - Build the core dashboards the business runs on and own external-facing reporting for board, warehouse-facility, and regulator audiences - Drive toward a single source of truth and governed self-service so the data function scales without becoming a bottleneck - Partner with Growth, Credit, Ops, Finance, and Treasury - clarify vague questions, end with concrete recommendations, and push back when requests are mis-scoped - Use AI tooling to accelerate mechanical work while owning the judgement and verification that keeps outputs honest

Requirements

- Approximately 5+ years across analytics engineering, data analysis, or BI, with seniority to own outcomes end-to-end and set standards from a blank slate - Hands-on ownership of a production data stack - pipelines feeding the warehouse plus a tested, documented, version-controlled model layer, not just occasional tool usage - Demonstrable business-facing analysis: instincts for which insights matter, stakeholder management, metric ownership, and reliably turning numbers into decisions - A thinking style oriented to definitions and trust - when numbers disagree, the instinct is to model and define once rather than patch another query - Deliberate, verified use of AI - speed up mechanical work while owning verification of the outputs - Strong git and testing fundamentals (non-negotiable); pragmatic judgement about fit-for-stage versus over-engineered solutions

Nice to have

- Experience with a modern data stack such as Fivetran for ingestion, Snowflake as the warehouse, and Hex for reporting, with the latitude to shape where the stack goes

Benefits and work setup

- Genuine founding ownership: define how the organisation models, defines, and trusts its numbers from day one rather than inheriting broken pipelines - A clear path to build and lead a data team as request volume and the business scale - Competitive compensation paired with an equity stake that reflects the founding nature of the role - AI-forward culture with full support from an internal product and engineering team - Hyper-growth phase backed by leading venture investors and global credit partners, with a mandate to apply AI to the mechanical work while owning the judgement that keeps numbers defensible