Additionalinformation

Additional Information

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.

Companydescription

Company Description

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.

Jobdescription

Job Description

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.

Qualifications

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