About the role
InPost Pay is building its own fintech platform from the ground up. It's a large-scale, international initiative where engineers will have the opportunity to shape the architecture, build core fintech components, and create solutions designed to operate at InPost's scale.
Responsibilities
•Own and prioritise the roadmap for feature stores, decisioning and underwriting datasets and self-serve analytics products.
•Sequence investment against commercial and risk impact.
•Translate credit, underwriting, fraud and commercial needs into clear, prioritised data-product specs and acceptance criteria that Data Engineering and ML teams can build against.
•Partner with the Head of Data Engineering & Analytics and the Head of ML & MLOps Engineering. Together align the roadmap with platform capacity, feature-store architecture and delivery sequencing.
•Set AI-first / agentic delivery as the team's default.
•Shape data products where agents are first-class components of the decisioning surface, with guardrails, evals and human-in-the-loop.
•Lead the Data Consultants to work AI-first.
•Define and track success metrics for the portfolio (adoption, self-serve usage, decisioning-data SLAs).
•Report progress and trade-offs to the Data Products & Management Director and cross-functional stakeholders.
•Lead up to 3 Data Consultants, who flex against demand across both domains. Responsible for their capacity planning.
Requirements
•Someone who loves data and loves turning it into products that move money-making decisions.
•Hands-on first. You interrogate data yourself with SQL and use AI as a daily tool.
•Experienced, with 5+ years in product management for data products or data platforms, including leading or coordinating technical and analytical teams (direct or matrixed).
•Experienced in fintech, credit, lending or payments (3+ years), with knowledge of BNPL origination, underwriting and transaction/acquiring flows.
•Strong in data-product strategy and roadmap ownership for feature stores and decisioning datasets.
•Fluent in point-in-time correctness, freshness, lineage and self-serve enablement.
•Skilled at converting business, credit-risk and ML needs into buildable, prioritised specs.
•Manages stakeholders and roadmaps across multiple product lines and delivery teams.
•Aware of the regulation that governs credit and payments data.
•Experienced in AI-first data-product leadership (2+ years): agents as part of the decisioning/data-product surface, with guardrails, evals, human-in-the-loop and control of agent-loop cost and latency.
•A leader who sets AI-first ways of working as the team's default, including LLM-assisted analysis and agentic tooling, and holds the team to designing with agents, not around them.
•Strategic and outcome-focused. Frames data-product decisions as ROI.
•Confident translating between Data Engineering / ML and business and risk stakeholders, and influencing without direct authority across Credit & Fraud Risk, Engineering and Commercial teams.
•Comfortable prioritising and re-prioritising a roadmap across two product lines under real capacity constraints.
•Fluent in English (B2+).
Nice to have
•Feature store or ML-enabled decisioning products.
•Acquiring / merchant payments data, including merchant acquiring and payment-scheme data structures.
•ML/MLOps fundamentals, enough to partner credibly with ML & MLOps Engineering.
•IFRS 9 / credit provisioning data concepts.
•Building self-serve analytics products for commercial or risk teams.
•Databricks / lakehouse platform familiarity.
Conditions
•A modern stack and a lean, AI-native team, with space to experiment with new technologies and bring them into production.
•A collaborative, fast-moving environment where autonomy and accountability go hand in hand.
•Hybrid, ideally one day a week in the Warsaw or Kraków office.
•B2B Contract.
