Director of Product Management, Data & Analytics

Responsibilities

Stabilise & Improve the Current State

• Take ownership of our existing data product — assess gaps, stabilise performance, and deliver near- term improvements that create immediate value for customers and internal teams.
• Audit and rationalise a portfolio of legacy custom reports built on stored procedures — understand the business problems they solve, document them, and define a clear migration path.

Define the Data Lakehouse Vision

• Define the vision and roadmap for data lakehouse, grounded in deep customer research and a clear understanding of where data solutions deliver the most value.
• Apply a Jobs to Be Done framework to uncover what customers are truly trying to accomplish and translate those insights into a prioritised product backlog.
• Conduct ongoing customer discovery with clients and customer-facing teams (Customer Success, Sales, Professional Services) to ensure the roadmap reflects real customer pain and opportunity.
• Work closely with the Data Architect and Database Engineer to communicate requirements clearly . You define the what and why; they own the how.
• Champion an AI-first approach to visualisation and insight delivery, evaluating and selecting third-party AI tooling rather than building bespoke visualisation layers.

Build & Deliver Data Products

• Define and ship customer-facing data products — including APIs, datasets, and consumption layers that customers or customer-serving teams directly use and derive value from.
• Productise data as a revenue stream — define how data and APIs are packaged, tiered, and commercialised for customers and partners.
• In partnership with product stakeholders, own the API product strategy — defining APIs not just as technical interfaces but as sellable, scalable products with clear value propositions, entitlement models, and a developer experience that delights customers.
• Partner with engineering to translate customer-driven requirements into clear, actionable briefs.
• Ensure data quality, governance, reliability, and documentation across all data products.

Own the AI Data Strategy

• Serve as the product owner of AI data foundation — defining what data needs to be available, in what form, and to what quality standard to power AI and ML workloads.
• Use customer research and Jobs to Be Done methodology to define intelligence layer product requirements; what AI-powered features and experiences customers need, handed off to technical teams for execution.
• Collaborate with AI strategy stakeholders to align the data product roadmap with AI product priorities.
• Champion AI-first approaches to data consumption: evaluating and recommending AI-powered visualisation and insight tools.

Lead & Grow the Team

• Begin as a senior individual contributor with direct leadership responsibility — you will be the data product function at initially.
• Work in close partnership with a Database Engineer and Data Architect, providing product direction and prioritisation while they drive implementation.
• Build out your team as the platform matures, with a planned trajectory to hire and lead a Product Manager and Product Owner.
• Act as a player-coach: credible enough to go deep on architecture and technical decisions alongside engineering peers, while driving strategy and stakeholder conversations at the executive level.
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Skills and Qualifications

Required Qualifications

• 8+ years of experience in data product management or related roles, with at least 3 years in a leadership capacity.
• Deep expertise in Jobs to Be Done and customer discovery — ability to uncover what customers truly need from data and translate that into a compelling product roadmap.
• Strong product instincts — knows how to prioritise ruthlessly, write clear requirements, and drive a backlog in an Agile environment.
• Solid working knowledge of data concepts (SQL, data modelling, APIs, ETL/ELT, BI tools), enough to be a credible partner to a Data Architect and Database Engineer.
• Experience defining and shipping data APIs as commercial products — including packaging, entitlements, and usage-based models.
• Understanding of data monetisation strategies — how to turn data assets into revenue-generating products.
• Strong understanding of AI and ML data requirements — how to define product needs for AI-powered features and intelligence layers.
• Familiarity with LLMs, generative AI, and AI-powered product surfaces — and how they consume and depend on structured, high-quality data.
• Strong stakeholder management skills — able to translate between engineering, product, and executive audiences.
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Preferred Qualifications

• Familiarity with Power BI source integration or similar third-party BI connectivity.
• Exposure to AI-powered analytics or visualisation tooling.
• Experience with dbt, Airflow, or modern data orchestration tools.
• Background in a SaaS or multi-product environment where data is fragmented across systems.
• Prior experience in financial data, market data, or fintech — relevant to domain