Data Products & Management Director - Fintech Engineering (m/f/n)

We are building a scale-up inside InPost. InPost Pay is growing from checkout into a full financial platform, and AI will flow through everything we do. You would join the core leadership team of this new business. The InPost executive team backs it, and you would be one of its key members. Our word is extreme ownership: you own the outcome, not just the function. You would be the first Data & AI leader, starting from a blank page, with one of the few proprietary datasets in European payments where checkout, payment and delivery behaviour meet.

Run data as a business. Build the AI-first engine of a new European fintech.

• 8+ years leading data engineering or ML organizations and 5+ years managing managers or team leads.
• Experience in financial services, payments, or lending data platforms including lakehouse, feature store, and point-in-time-correct data serving.
• Fluent in Databricks/Spark at scale.
• Experience building agentic systems in production (3+ years).
• Comfortable interacting with VP/C-level stakeholders.
• Fluent in English (minimum B2 level).

Bonus points for:

• Regulated fintech or banking experience
• Experience scaling a data function from zero
• Vendor negotiation skills
• Multi-jurisdiction architecture knowledge
• MBA degree

• Build agent-first systems with production-grade guardrails, evaluations, and observability.
• Develop and maintain a state-of-the-art ML and MLOps stack ensuring models are built, validated, served, and retrained properly on a governed Databricks lakehouse.
• Create and manage one unified data and ML platform including feature store, decisioning layer, and analytics.
• Establish and own data products with clear business cases, roadmaps, and measurable monetary outcomes.
• Ensure fast, explainable, and auditable credit and risk decisions in real time.
• Integrate governance (GDPR, CCD2, DORA) as an enabler for faster shipping than competitors.
• Foster a culture of experiments where every model and decision is tested and measured for value.
• Manage the function's budget, headcount, return on investment, and accountability for results.
• Build organization from scratch including Data Engineering & Analytics, ML & MLOps, and Data Product teams.
• Deliver board-level reporting on platform readiness, governance, and delivery risk.