Data Engineering Lead (AI, Analytics & Platform)

Department: Tech

Location: Sydney

Employment Type: FullTime

About Fetch

Fetch is how modern pet parents give their pets the best care 🐶🐱. One pink app 🩷 for insurance, health, and care – easy, fair, and kinda fun. We’ve raised our Series A from Lightspeed and Airtree, customers love us (rated #1 by Choice Magazine!), and we’re growing fast.

We’re looking for a sharp, ambitious data leader to build the foundation that powers pricing, product, claims, operations, and AI – so millions of pets get smarter, faster, fairer care.

• Location: Sydney (Hybrid – Tue/Wed/Thu in-office)

• Compensation: Series A salary + meaningful equity upside

• Stack: Python, Typescript, SQL, dbt, event infra, GCP/BigQuery (and whatever else gets the job done)

• Tooling: Cursor, Claude, and unlimited AI tools – no token limits

Your role

You’ll own Fetch’s data platform end-to-end – from ingestion and modelling to observability, experimentation, and AI evaluation. You’ll work directly with product, pricing, ops, and AI engineers to turn noisy, real-world events into clean, trusted, real-time data that powers decisioning, agents, and automation.

You’ll design the data foundations that make AI safe, measurable, and reliable: datasets, evals, feedback loops, and monitoring that keep our agents honest.

• 🧱 Build and own Fetch’s entire data stack – ingestion, pipelines, warehouse, observability

• 🤖 Partner with AI engineers to productionise agents and models with clear metrics, feedback loops, and evaluation frameworks

• ⚡️ Make data flow in real-time across pricing, product, claims, ops, and AI

• 🔁 Automate everything – alerts, tests, and fail-safes so nothing breaks silently

• 📊 Enable smarter pricing, sharper decisions, and clearer insight across the business

• 🤝 Partner with engineering and AI teams to productionise data-driven features

What you’ll build

• High-quality data ingestion & modelling: Clean, well-documented pipelines from product, vet systems, claims, support, and external partners into consistent models that everyone can trust

• Real-time data flows: Event-driven pipelines that power pricing decisions, fraud checks, risk models, and operational dashboards in minutes, not days

• Analytics-ready warehouse: A robust warehouse (e.g. BigQuery) with clear, tested dbt models that make it easy for teams to self-serve, explore, and experiment

• Agent & AI evaluation loops: Datasets, labels, and evaluation pipelines to measure and improve AI agents (e.g. support, risk, health) – including offline evals, guardrail checks, and online performance tracking

• Feedback & human-in-the-loop workflows: Data-driven feedback loops to capture human overrides, corrections, and edge cases – turning them into training and eval data

• Monitoring & observability: End-to-end data observability – schema change detection, freshness checks, anomaly alerts, and clear runbooks so data issues are caught before they hit stakeholders

• Automation & workflows: Data-driven jobs that trigger notifications, workflows, and AI features – with strong audit trails and safe rollback paths

About you

You hate missing data, broken pipelines, or dashboards that don’t reflect reality. You build obsessively clean, reliable systems, automate relentlessly, and spot problems before they break.

You move fast, go deep, hold a high bar for quality, and never wait to be asked. You’re sharp, direct, and care deeply about doing great work.

• 5+ years building and maintaining data platforms or analytics engineering stacks at scale

• Strong with Python and SQL, and comfortable with dbt, modern warehouses, and event-driven data

• Experience designing reliable batch and/or streaming pipelines with strong observability and testing

• Pragmatic builder – you know when to ship a simple solution and when to invest in scalable architecture

• You care about product: you like to understand the business problem, challenge requirements, and push for outcomes over output

• Obsessed with data quality, trustworthiness, and clear definitions (metrics, contracts, schemas)

• Clear communicator, effective collaborator across engineering, product, ops, and leadership

• Bonus – experience designing evaluation frameworks for AI/LLM systems (offline evals, golden sets, regression tests, monitoring)

• Bonus – experience supporting AI agents or ML products (feature engineering, feedback loops, human-in-the-loop systems)

• Bonus – experience in insurance, healthcare/veterinary, fintech, or other regulated environments

What it’s like here

We’re ambitious, collaborative, and genuinely enjoy building together. The Fetch team is smart, thoughtful, and kind – low ego, open, caring, and always supportive.

• 🧠 You’ll be involved early in strategy. You’re encouraged to give your opinion and debate with founders and the rest of the team

• 🤪 Weird is welcome. We value unexpected perspectives and people who think differently, so just be you

• 🤖 Unlimited AI tooling – no token limits or approvals needed. Just try things

• 🩷 Work on a product genuinely loved by thousands of pets and pet parents

• 🚀 We’re growing FAST. It’s an exciting time to join and you’ll directly shape how data powers our products and decisions

And the perks:

• 📈 Competitive Series A salary + meaningful equity

• 🏠 Hybrid working (3 days Sydney office, flexible WFH)

• 💻 Latest MacBook Pro and a top setup

• ✈️ Two team retreats each year (Blue Mountains, SXSW, Hunter Valley)

• 🐶 Office dogs for cuddles and interruptions

• 🍫 Bean to cup coffee machine, unlimited fruit and snacks. Toblerone on-tap

How to apply

Apply via the link, along with a quick note highlighting (bonus points if you include a photo of your pet 🐾):

• The most impressive data or analytics system you’ve built (why it mattered, what was tough, lessons learned)

• How you’ve improved data reliability or trust (tests, observability, contracts, or process)

• Links to your work (GitHub, portfolio, dashboards, talks, or writing)

Interview Process

• Intro chat: Mutual fit, role scope, and what you’re excited to build

• Technical deep dive: Past data systems you’ve shipped, tradeoffs, and how you drive outcomes

• Practical design challenge: A realistic problem (data architecture + product thinking)

• Meet the team: Working style, collaboration, and day-to-day at Fetch