Data Product Lead - Fintech Engineering (m/f/n)

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SeniorHybryda·WarszawaFull-time

#474639·Dodano wczoraj·0

Źródło: InPost

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Tech Stack / Keywords

SQL Databricks LLM MLMLOpsAIfeature storeIFRS 9

Podsumowanie oferty

Own the data products that support credit and payments decisions across BNPL and Acquiring & Payments. Set their roadmap and translate business, credit, fraud and commercial needs into buildable specifications for Data Engineering and ML teams. Lead an AI-first approach to decisioning products using agents, guardrails and evaluation.

Wymagania

At least 5 years of product management experience for data products or data platforms, including leading or coordinating technical and analytical teams.
At least 3 years of experience in fintech, credit, lending or payments, with knowledge of BNPL origination, underwriting and transaction/acquiring flows.
Hands-on experience interrogating data with SQL and using AI as a daily tool.
Strong data-product strategy and roadmap ownership for feature stores and decisioning datasets; fluency in point-in-time correctness, freshness, lineage and self-serve enablement.
Ability to convert business, credit-risk and ML needs into buildable, prioritised specifications and manage stakeholders and roadmaps across product lines and delivery teams.
Awareness of regulations governing credit and payments data.
At least 2 years of experience in AI-first data-product leadership, including agents in decisioning/data products, guardrails, evaluations, human-in-the-loop processes, and control of agent-loop cost and latency.
Experience establishing AI-first ways of working, including LLM-assisted analysis and agentic tooling.
Strategic, outcome-focused approach to data-product decisions and ROI.
Ability to translate between Data Engineering/ML and business and risk stakeholders, and influence across Credit & Fraud Risk, Engineering and Commercial teams.
Comfortable prioritising and reprioritising a roadmap across two product lines under capacity constraints.
Fluent in English (B2+).

Nice to have:

Feature-store or ML-enabled decisioning product experience.
Acquiring or merchant payments data experience, including merchant acquiring and payment-scheme data structures.
ML/MLOps fundamentals.
Knowledge of IFRS 9 or credit-provisioning data concepts.
Experience building self-serve analytics products for commercial or risk teams.
Familiarity with Databricks or lakehouse platforms.

Obowiązki

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 prioritised data-product specifications and acceptance criteria for Data Engineering and ML teams.
Align the roadmap with platform capacity, feature-store architecture and delivery sequencing in partnership with the Heads of Data Engineering & Analytics and ML & MLOps Engineering.
Set AI-first and agentic delivery as the team’s default; shape data products with agents as components of the decisioning surface, including guardrails, evaluations and human-in-the-loop processes.
Lead Data Consultants to work AI-first.
Define and track portfolio success metrics, including adoption, self-serve usage and decisioning-data SLAs; report progress and trade-offs to the Data Products & Management Director and cross-functional stakeholders.
Lead up to three Data Consultants and plan their capacity across both domains.
Work with feature stores, decisioning and underwriting datasets, a Databricks lakehouse platform, self-serve analytics products, LLM-assisted and agentic tooling, and data from alternative-data providers and payment-scheme/acquirer partners.

InPost

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