Principal Engineer

Lexer is a Customer Data Platform that’s been helping retail and hospitality brands understand and act on their customer data for seventeen years. We’re now rebuilding what a CDP can be: an AI-native platform where the hard work of segmentation, analysis, and activation happens through conversation, not configuration. That’s a significant engineering undertaking, and it changes what great engineering looks like here.

We’re looking for a Principal Engineer to work with our AI product teams on the hardest technical problems we have.

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About the role

This is the most senior individual-contributor engineering role at Lexer. You’ll shape how the engineering organisation builds, through demonstrated technical authority and the quality of the systems you create. You’ll work across every stream, taking on the highest-leverage problems and lifting the standard of engineering around you.

At its core this is a software engineering role. You're building and operating a SaaS and data platform product that our customers rely on every day — multi-tenant, always-on, and held to a high bar for quality, performance and reliability. Strong software engineering fundamentals are the foundation everything else here is built on.

We’ve transitioned to agentic harness engineering and spec-driven development, where engineers direct AI agents and maintain the systems that produce software rather than writing all of it by hand. You work fluently this way and you push the practice forward, building the guardrails, specifications, and architectural practices that let agents move at speed without eroding the system. We believe good engineering practice matters more in the age of AI, not less: agents amplify whatever system they work in, so clean ones get faster and messy ones get worse. Making ours the kind that compounds is the heart of this role.

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Role details

• Level: Principal Engineer — the most senior individual-contributor role in engineering.
• Reports to: CTO.
• Experience: 8+ years of hands-on software engineering, including significant time operating at a senior/staff level across complex systems.
• Location: Remote or Hybrid Melbourne-based, hybrid — 3 days a week in our St Kilda office, 2 days working from home. We’ll also consider remote candidates with strong, consistent overlap with AEST/AEDT core hours.

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How we work

We practice spec-driven development inside an agentic harness: engineers direct AI agents and maintain the systems that produce software. Our engineering culture draws on XP — test-first, continuous integration, small batches, pairing, and shared ownership. This is a hands-on, collaborative role, so being comfortable working closely with others in real time matters as much as working independently.

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Our stack

We have a predominantly Python and React stack, running on AWS, with data processing in Databricks. You’ll be equally at home in the data and processing layers as in the interfaces customers touch, and comfortable making architectural decisions that span the whole system.

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What you’ll do

• Work hands-on, in the codebase, across engineering streams on the problems that matter most: the AI capability layer, the data contracts that hold the platform together, and the ongoing modernisation of the platform.
• Hold architectural direction as a living function rather than a static document, making the calls that keep the system coherent as it changes. You’ll own the ADR process so those calls are captured and understood.
• Keep the architecture adaptable as the business changes, favouring low coupling, high cohesion, and strong boundaries so the system is resilient, easy to build on, and easy to maintain.
• Design for reliability and observability from the start, so the platform is dependable and its behaviour is understood.
• Build the guardrails that keep agentic engineering safe at speed, including spec-driven development, architectural practices, context engineering, and the test coverage that lets agents move fast without breaking things. You’ll push the practice forward across the team.
• Lift the engineers around you through pairing, code review, and the standard your own work sets.

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What success looks like

In your first 3 months

• You’re hands-on in the team building our new AI flagship product — a strong contributor shipping real product towards PMF, adapting our agentic harness as we go, and running the ADR process.

By 6 months — caretaker of scale

• As the platform grows, you’re the caretaker of scale — keeping our systems adaptable for new market opportunities and bringing the right amount of rigour and process for our evolving scale, ensuring the infrastructure is fit for purpose.

By 12 months — across the whole platform

• You work across multiple teams, ensuring the holistic platform works — all the systems fitting together coherently — and you’re recognised as a technical authority who raises the bar across engineering.

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What you’ll bring

Must have

• Strong software engineering fundamentals, with a track record of building, shipping and operating production SaaS at scale — you care about the craft: clean, well-tested, maintainable code and healthy CI/CD.
• Deep hands-on engineering across complex systems, ideally with data-intensive or platform work.
• Practical experience directing AI coding agents to ship production software, and building the guardrails and architectural practices that make it safe at scale.
• A track record of holding architecture across a whole system, not just a single team.
• Strong communication. You make complex technical directions clear to engineers and non-engineers alike, and you can model and communicate system designs visually, including with C4.
• Comfort working in a mature, evolving codebase; you can navigate real complexity and still move fast.
• Strength across our stack: Python (or similar), React and AWS.

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Strong preference

• Background in CDP, martech, or data platform domains.
• A foundation in SOLID architectures and XP-style practice — test-first, continuous integration, small batches, shared ownership.

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Nice to have

• Experience re-platforming legacy stacks toward modern architectures.