Job Summary

Job Title: Data AI Product Manager - Finance

Location: Barcelona, Spain

Mode: Hybrid

This role is a high-impact Data AI Product Manager position focused on Finance, at the center of a large-scale transformation to build a modern, AI-ready finance data ecosystem. You will act as the critical bridge between Finance business stakeholders, data modelers, engineers, architects, and AI teams, translating finance priorities into scalable data and AI products that deliver measurable business outcomes. With ownership of end-to-end finance data products, you will define product vision, manage prioritization, drive agile execution, and ensure solid alignment between technology delivery and Finance business value. The role offers the opportunity to embed a Data-as-a-Product mindset across Finance, shaping trusted foundational datasets, data lake and warehouse assets, semantic layers, dashboards, and AI-enabled use cases across areas such as record-to-report, controlling, planning, consolidation, working capital, intercompany, tax, treasury, and performance management. Ultimately, it is a strategic position where you connect enterprise finance data investments to improved decision-making, automation, governance, and AI readiness.

Responsibilities

- Define the product vision and roadmap for Finance Data AI products, aligned with Finance strategy, enterprise data priorities, and measurable business outcomes.
- Translate Finance business needs into clear data product capabilities, technical priorities, user stories, and acceptance criteria.
- Own and manage the Finance data product backlog, including prioritization, sequencing, dependencies, value definition, and delivery clarity.
- Lead sprint,



quarterly, and release planning for Finance data and AI product teams, ensuring alignment with Finance stakeholders and platform teams.
- Collaborate with Finance SMEs, data engineers, architects, data modelers, governance teams, and AI specialists to build trusted, scalable, and reusable finance data solutions.
- Drive delivery of foundational Finance data assets, including curated datasets, semantic models, reporting layers, data quality controls, lineage, and AI-ready data products.
- Partner with Finance process owners to understand and improve data flows across accounting, controlling, consolidation, planning, tax, treasury, intercompany, and management reporting.
- Ensure finance data products meet expectations for data quality, auditability, reconciliation, security, access control, and regulatory or compliance needs.
- Identify opportunities to apply AI, automation, and advanced analytics to Finance use cases such as anomaly detection, variance explanation, forecast support, root-cause analysis, and self-service insights.

Requirements

- Strong understanding of enterprise finance data ecosystems, including data ingestion, modelling, transformation, semantic layers, analytics, AI enablement, and data governance.
- Ability to write clear product requirements, user stories,



and acceptance criteria.
- Strong analytical skills and ability to work with Finance stakeholders to identify business problems, define value cases, and translate them into actionable data and AI product requirements.
- Ability to communicate complex finance, data, and AI concepts clearly to both technical teams and non-technical Finance stakeholders.
- High ownership and organizational skills with outcome-driven execution.
- Familiarity with cloud data platforms and modern analytics ecosystems, such as AWS, Azure, GCP, Databricks, Snowflake, Power BI, dbt, data lakes, data warehouses, and lakehouse architectures.
- Finance domain expertise, preferably across accounting, controlling, record-to-report, planning, consolidation, intercompany, tax, treasury, working capital, or performance management.
- Experience working with Finance systems and data structures, including SAP ECC, SAP S/4HANA, Central Finance, SAP Group Reporting, BW, planning tools, and finance master data.
- Understanding of core Finance data objects and concepts such as general ledger, chart of accounts, company code, profit center, cost center, internal orders, assets, vendors, customers, open items, journal entries, actuals, budgets, forecasts, and consolidation data.
- Awareness of Finance control requirements, including reconciliation, traceability, auditability, data lineage, segregation of duties, access management, and sensitive financial data handling.
- Ability to define and monitor product success metrics, adoption, data quality KPIs, and business value realization for Finance data and AI products.

📌 Data & AI Product Manager - Finance (Hyderabad)
🏢 DSM Firmenich
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