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.

We’re building a lean, highly effective engineering organization in Poland, with AI-native engineering at its core. We’re looking for strong engineers who are comfortable with agentic engineering, from experienced professionals building complex systems and engineering tooling to a new generation of engineers who have embraced AI-driven development from the start of their careers.

The mission

Own the data products that every credit and payments decision at InPost FinTech depends on, across both BNPL and Acquiring & Payments.

• Roadmap with impact. You own and prioritise the roadmap for feature stores, decisioning and underwriting datasets and self-serve analytics products. You sequence investment against commercial and risk impact.
• Requirements that can be built. You 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.
• Aligned with the platform. You partner with the Head of Data Engineering & Analytics and the Head of ML & MLOps Engineering. Together you align the roadmap with platform capacity, feature-store architecture and delivery sequencing.
• Agent-first by default. You set AI-first / agentic delivery as the team’s default. You shape data products where agents are first-class components of the decisioning surface, with guardrails, evals and human-in-the-loop. You lead the Data Consultants to work AI-first.
• Outcomes, not feature lists. You define and track success metrics for the portfolio (adoption, self-serve usage, decisioning-data SLAs). You report progress and trade-offs to the Data Products & Management Director and cross-functional stakeholders.
• A team that flexes with demand. You lead up to 3 Data Consultants, who flex against demand across both domains. You are responsible for their capacity planning.

What you will work with

The feature store, decisioning and underwriting datasets, a Databricks lakehouse platform, self-serve analytics products, and LLM-assisted and agentic tooling. You will also work with data from alternative-data providers and payment-scheme/acquirer partners.

You are:

• 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. You are 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. You manage 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. You frame 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+).

Bonus points for:

• 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

Why InPost?

• Architectural authority from day one. You set the standard.
• Greenfield, high stakes. Your design decisions last for years.
• 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.
• We offer a B2B Contract.