Product Manager - Data & Analytics

Job Description As Product Manager for Data and Analytics, you will own the discovery, definition, and delivery of Keystone AI's Data and Analytics product capabilities within the Embedded Payments team. This includes merchant-facing reporting and insights, internal analytics infrastructure, payment data quality and the data layer that powers AI features across the Embedded Payments platform. You will sit at the intersection of fintech data engineering, product analytics, and commercial intelligence — turning raw payment, merchant, and transaction data into products that drive measurable value for Keystone AI, our partners, and their merchants. What You'll Do Data Product Roadmap Define and own the Data & Analytics product roadmap for Embedded Payments, prioritising ruthlessly across merchant insights, risk intelligence, and internal data tooling. Work with the Lead Product Manager and Director for Engineering for Data to define and execute the product vision for the Global Embedded Payments platform. Merchant Analytics Own the merchant-facing reporting and insights product — including transaction dashboards, settlement reports, reconciliation tools, and cohort-level performance analytics. Define self-service analytics capabilities for our SaaS BMS’s: embedding data and insights directly into partner software products via APIs or embeddable components or bringing to life in our our Payments Merchant Portal. Work with Design and Engineering to build intuitive, accurate, and performant data experiences — ensuring merchants can act on their data without requiring analyst support. Establish feedback loops with the internal teams to continuously improve analytics utility and drive product stickiness. Payment Data Quality & Governance Define and enforce data quality standards across the Embedded payments data pipeline — from transaction ingestion through to reporting and downstream AI models. Own the data product's semantic layer: ensuring consistent definitions for metrics like TPV, net revenue, activation rate, chargeback ratio, and settlement accuracy across all surfaces. Work with Engineering and Data Engineering to build observable, trustworthy data pipelines with SLA-backed freshness and accuracy guarantees. Develop a data governance framework appropriate for a PCI-DSS environment — covering data classification, access control, retention, and merchant data privacy obligations across APAC, UK, and NA jurisdictions. Risk, Fraud & Compliance Intelligence Collaborate with Risk, Compliance, and Engineering to define data products that power fraud detection models, dispute management workflows, and AML screening. AI-Augmented Product Practice Embed AI tools across the analytics product lifecycle: use LLMs to accelerate synthesis of data quality issues, competitive teardowns, stakeholder interview notes, and PRD drafts — operating at materially higher output than a traditional PM. Define the product requirements for AI-powered analytics features within the Global Embedded Payments Platform: natural language querying of payment data, anomaly explanation, and predictive merchant health scoring. Work closely with Data Engineering to scope, validate, and ship ML-powered features — including evaluation criteria, feedback loops, and guardrails for model behaviour. Champion AI tool adoption within the Embedded Payments product team and document effective workflows that peers can replicate. Stakeholder & Cross-Functional Collaboration Partner with Finance, Risk, Compliance, Commercial, and Engineering to align on data definitions, KPI frameworks, and reporting standards. Serve as the primary product manager for data squad deliverables — running sprint ceremonies, writing acceptance criteria, and managing backlog prioritisation. Present data product roadmap and outcomes to senior leadership on a regular cadence. Qualifications Required 3+ years of product management experience, with at least 2 years owning data, analytics, or intelligence products in a fintech, payments, or financial services context. Strong understanding of payment data: transaction flows, settlement, reconciliation, chargeback lifecycle, and how each generates structured data with specific quality and latency requirements. Hands-on SQL proficiency — comfortable writing and interpreting queries to validate data, investigate anomalies, and size opportunities without needing analyst support. Experience working with modern data stacks: data warehouses (Snowflake), BI tools (PowerBI, Tableau, Metabase), and event tracking platforms. Proven ability to translate ambiguous business problems into well-scoped data products with clear success metrics. Demonstrated understanding of data governance, privacy regulation (GDPR, PDPA, or equivalent), and data quality frameworks in a regulated fintech environment. Proficiency with AI productivity tools (Claude, ChatGPT, Cursor, or equivalent) — with concrete examples of using AI to increase PM output quality and speed. Comfortable wo