Who are we?

We are Australasia’s largest privately owned aggregator group, backing more than 6,000 brokers and advisers across Australia and New Zealand. Partnering with over 110 banks and lenders, we support independent businesses alongside our own Loan Market brand. In FY26, our brokers helped over 400,000 people settle over $167 billion in loans and reached a collective loan book of $428 billion, proof of what happens when scale meets real drive.

We live our values in everything we do. Compassion for our customers, doing what we say, being curious and thinking openly, going further together. We’re ambitious, but we keep our feet on the ground, always hungry, always humble, and focused on being the absolute best at what we do.

We were proud to be named Australian Financial Review BOSS Best Place to Work - Banking, Superannuation and Financial Services & in 2025 and one of Mortgage Professional Australia's Top Mortgage Employers for 2026!

About the role -

A player-coach role: build the hardest things, and grow the people building the rest. As a Data Engineering Lead you are accountable for what your team builds and whether it works. You will spend roughly 80% of your time doing the engineering and 20% leading, and both halves are the job.

You will hold the technical standard of a Principal Data Engineer, take the hardest work on the team, and lift direct reports whose growth you own. We are explicit that this role stays hands-on. If it stops being hands-on, something has gone wrong and we will fix it rather than quietly redefine the role.

This is the right role for a senior or principal data engineer who wants to lead without stepping away from building. Reporting to the GM Data and AI Enablement, this role manages a team of Data Engineers. Some of your team will be aligned to Platform outcomes, others may be embedded into Missions delivering outcomes.

We will also consider Sydney applicants.

What you will do -





- Design and build the platform the team runs on: ingestion, orchestration, integration patterns and the deployment path, without becoming the bottleneck, and deliberately delegating problems so your team grows.
- Build and run the platform behind LMG's corporate machine learning and generative AI: deployment and serving, model registry, retrieval and vector infrastructure, monitoring, retraining and guardrails. Data Engineering owns this runtime. Software Engineering owns anything customer-facing, and you own the handover when a solution crosses that line.
- Set technical direction for your team's data domain, hold the definition of done, and make trade-offs explicit rather than implicit.
- Partner with Technology, set standards, runbooks and playbooks: the quality bar, how work gets reviewed, how a system is recovered, how an incident is handled, and how governance, consent, retention and audit obligations are met.
- Turn ambiguous business direction into a sequenced plan the team can deliver, and communicate changes early with their cost.
- Run one-to-ones, own growth plans, give feedback close to the event, and write and deliver performance reviews.
- Be accountable for service levels, issue response, run cost and technical debt, systems failing safely and visibly, and keep the team's operational load sustainable and within working hours.
- Own the technical hiring loop for your team and own new starters' first ninety days.

What you will bring -

- The technical depth to be the strongest engineer in the room without needing to be.
- Experience growing engineers, including the tough conversations.




- Deep hands-on experience building and running cloud data platforms; ingestion, orchestration, transformation and the deployment path.
- Experience putting machine learning or generative AI into production, or the platform depth and appetite to own it.
- A track record of delivering through other people as well as through your own work.
- The judgement to know which problems to take yourself and which to hand away.
- The instinct to notice when someone on your team is struggling before they say so.
- Core Programming - Python and advanced SQL
- Cloud & Modern Warehouses - AWS preferred, Snowflake or Databricks
- Distributed Engines and Streaming - Kinesis, Kafka
- Orchestration - Github actions, AWS step functions
- Infra as code - terraform

Perks -

- Flexibility & Balance: Hybrid work (min. 2 office days per week, including a team anchor day), 6 additional ‘Me’ leave days, flexible public holiday swaps, and options to purchase additional annual leave.
- Family & Wellbeing: 16 weeks paid parental leave (plus superannuation support), paid fertility treatment leave, 12 days personal leave, and access to the Sonder wellbeing app.
- Growth & Development: Dedicated paid study leave and L&D; programs for each stage of your career
- Community: Participation in the Loan Market Group Foundation (with donation matching) and a paid volunteer day.

Using AI in your application: At LMG we embrace innovation, and we are comfortable with candidates using AI tools to assist in preparing their application. We encourage you to use these tools to polish your resume but please be mindful that your application should remain an honest representation of your own experience and qualifications. We are keen to get to know your true capabilities and how your specific background aligns with our team, which we will explore further in our conversations and interviews.

📌 Data Engineering Lead (Melbourne)
🏢 LMG
📍 Melbourne