Technical Product Owner

Technical Product Owner

About the Company

JAVLN helps insurance intermediaries evolve into super powered risk advisers. Our vision is to be the global, AI-native operating system for insurance intermediaries: the gold standard for how risk is managed in the digital age.

We're building a world class company across our products, customer experience, financial performance and culture. That takes A-players who drive change rather than wait for it, pairing sharp thinking with deep industry expertise.

Four values shape how we work: we are team players, we relentlessly innovate, we care about the work we do and we keep things simple.

Role Overview

This Technical Product Owner role is built for someone who has lived in a hybrid Technical Business Analyst and Product Owner role and wants to be part of the next frontier of delivering AI-first SaaS products.

The work you'll do decides whether a feature succeeds: understanding the customer, writing specifications that are precise enough for an agent to build from and the judgement call at each decision gate. The delivery mechanics that fill most Product Owner calendars? AI agents will handle those.

You'll sit inside a JAVLN AI Value Pod, working closely with a Product Manager and Senior Software Engineer alongside a set of AI agents, shipping features on JAVLN Officetech, one of our globally successful SaaS products. The agents write the code, the tests and the documentation, the humans instruct and verify.

A Value Pod is a small team embedding AI into a disciplined, governed lifecycle. Humans make the decisions that matter, controls are automated, and continuous validation of AI output means the pod delivers in days rather than weeks.

The Product Manager sets direction and decides what matters most across the roadmap. You decide how that direction becomes a sequenced, buildable backlog, and what "done" means for each item.

If you have been waiting for a role where AI fluency is the job rather than a line in the tooling section, this is it!

Position Details

• Position Title: Technical Product Owner
• Function: Product
• Reports To: Head of Document Intelligence
• Direct Reports: None (you will direct AI agents, not people)
• Location: Auckland, New Zealand
• Date: August 2026

Key Responsibilities

• Requirement elaboration and business analysis: Own the detailed analysis. Translate customer and business requirements into clear, unambiguous requirements, user stories and acceptance criteria. Map user journeys. Identify gaps, conflicts and dependencies. These outputs give AI agents the precision they need to deliver great outcomes.
• Customer representation: Be the voice of the customer within the pod, so delivered features solve real user problems and reflect genuine business value rather than technical completeness alone.
• Technical fluency: Engage directly with customers to understand and capture the high-level technical elements of what they need, which may include data modelling, integrations and API contracts, so requirements are accurate, validated and a solid starting point for the engineering work that follows.
• Validation and quality: As part of authoring the requirements specification, define acceptance criteria, design test scenarios, and work with AI to build and run automated tests. Review and validate AI-generated work against requirements on an ongoing basis to catch deviations, gaps and risks early, and assess the quality of delivered features against the specification.
• Backlog ownership and prioritisation: Own, order and continuously refine the product backlog within the direction set with the Product Manager. Manage dependencies so the backlog reflects near real-time priorities at a cadence suited to delivering in small sequential batches.
• Outcome measurement: Define, capture and track product feature KPIs to give ongoing, quantifiable measurement of feature success.

Key Skills and Experience

• Proven Product Owner experience: 6+ years as a Product Owner or equivalent, owning a backlog and delivering value.
• Business analysis capability: Strong, hands-on business analysis skills covering requirement elicitation, user-story mapping, gap analysis and precise acceptance criteria.
• Technical fluency: Able to work closely with an engineer to understand and reason about high-level technical elements such as integration requirements, data and API contracts, security and governance. Comfortable with modern web user interfaces, domain/data modelling, APIs and API data formats, and competent using SQL for data analysis. A background working closely with engineering teams is expected.
• Agentic tooling: Hands-on experience with agentic coding tools such as Claude or Copilot, and examples of using modern AI tools to transform traditional processes and outcomes.
• Non-functional requirements (advantageous): Confidence capturing and documenting non-functional requirements, for example performance, security and scalability.
• Product analytics (advantageous): Experience defining and tracking product KPIs and working with analytics or product data to evidence outcomes.

• Specification excellence: Exceptional ability to write clear, specific, unambiguous requirements and tests. In an AI-first pod this is the highest leverage skill you have.
• AI ways of working: Enthusiasm for AI-driven development and comfort working alongside agentic tools. Curiosity, adaptability and a growth mindset for AI is critical for this role.
• Decisiveness and accountability: Sound judgement and the confidence to approve or challenge specifications, representing JAVLN and customer interests as the accountable human at each decision gate.
• Communication and collaboration: Excellent written and verbal communication, and the ability to work effectively in a small, cross-functional pod. You will facilitate synchronous collaboration, drive rapid decision-making and partner closely with a Product Manager.

Key Performance Indicators

• Specification quality: Features pass validation against acceptance criteria first time, with minimal rework attributable to unclear or incomplete requirements.
• Delivery cadence: The backlog stays refined and sequenced ahead of pod capacity, supporting delivery in small sequential batches measured in days.
• High quality: Customer facing outputs meet high quality standards with low regression.
• Feature outcomes: Every shipped feature has defined KPIs, tracked from release, with results reported back to the pod and Product Manager.
• Validation effectiveness: Deviations, gaps and risks in AI-generated work are caught at the decision gates, before release rather than after.

Live the Values

• We are TEAM players
• We relentlessly INNOVATE
• We CARE about the work we do
• We keep things SIMPLE