Senior Product Manager

As SPM for User Identity and Profiling, you will own the product strategy and execution for the company's identity infrastructure and user profiling platform. This is a foundational platform role: your work will unlock personalisation, improve recommendation accuracy, reduce friction in return journeys, and give the company a durable data asset at the household level.
The candidate will have responsibilities across the following functions:
User identity platform:
Define and own user identity model: anonymous-to-known resolution, cross-session stitching, cross-vertical identity unification, and household-level aggregation.
Own the identity graph product requirements: entity resolution, deduplication logic, confidence scoring, and merge/split rules.
Design identity persistence across channels: web, app, partner white-label, and inbound call/agent flows.
Own the privacy and consent architecture: what data is captured, how it is stored, what user controls exist, and how consent propagates across the platform.
Ensure the identity layer meets Australian privacy and data handling standards relevant to the verticals.
User profiling and household model:
Define the household profile schema: what attributes are captured, inferred, and maintained across Energy, Telecom, Insurance, and Finance verticals.
Own the profile completeness and freshness model: how profiles are built over time, how stale data is flagged, and how users can view and update their own profiles.
Define the event taxonomy and data capture requirements that feed the profiling engine, in partnership with the Data Engineering team.
Build the product foundation for proactive recommendations: what signals trigger a recommendation, what confidence threshold is required, and how recommendations are surfaced.
Identity as a platform service:
Package identity and profiling as internal platform APIs consumed by vertical PMs, the recommendation engine, and AI features.
Define service contracts: what the identity API provides, what data consumers can query, and what change management process applies.
Own the identity SDK or integration layer for white-label partners who need to pass user context into the company.
Establish identity data quality metrics: completeness rate, resolution accuracy, stale profile rate, and cross-vertical linkage coverage.
Personalisation foundation:
Work with vertical PMs and the AI/Automation SPM to define how profile data powers personalised comparison surfaces, pre-filled forms, and contextual recommendations.
Define the personalisation feedback loop: how user actions update the profile, how recommendation outcomes are measured, and how the model improves over time.
Requirements:
6-10 years of Product Management experience with a strong focus on identity systems, user profiling, personalisation platforms, or customer data platforms (CDPs).
Has owned an identity or profiling platform in a consumer-tech context: login/auth infrastructure, user graph, or household/account modelling.
Experience with anonymous-to-known identity resolution, cross-device stitching, or session continuity problems in high-traffic consumer products.
Demonstrated understanding of privacy-by-design: consent management, data minimisation, and user-facing data controls.
Background in FinTech, InsurTech, e-commerce, marketplace, or aggregator platforms preferred. Comparison or subscription-switching context is a strong plus.
Technical and data fluency (must-have):
Comfort specifying data models, entity relationships, and schema evolution requirements as product artefacts.
Working understanding of identity graph concepts: entity resolution, probabilistic vs deterministic matching, confidence scoring.
Ability to write clear data product requirements: event schemas, API contracts, data quality SLAs.
Familiarity with CDP architecture, data pipelines, or event-driven data capture patterns enough to partner credibly with data engineering.
Capabilities and ways of working:
Long-horizon thinker: identity is a foundation, not a feature. Must be able to hold a 2-3 year architecture view while shipping incrementally.
Privacy-first instinct: thinks about data collection and user consent before thinking about what data would be nice to have.
Platform mindset: builds for reuse across verticals, not for the nearest vertical asking for it.
Data-literate: can work directly with data engineers and analysts to define, validate, and measure profile quality.
Cross-functional influence: identity touches every vertical, and every team must drive alignment without owning everything.
Tools and education:
Jira, Confluence, and modern PM tooling.
Familiarity with identity tooling (Auth0 Okta, or equivalent), CDP platforms (Segment, mParticle, or equivalent), and data pipeline concepts.
Bachelor's degree in Engineering, Computer Science, or related field preferred.

Experience
6-10 yrs