AI Data Governance & Strategy Lead

Comarch is a company undergoing a grand-scale transformation with a focus on AI data governance and strategy across multiple sectors. The role involves working closely with sector Heads of AI/CTOs, Product, R&D, Security/Compliance, and the Chief AI Officer to ensure AI investment delivers measurable business value.

• 10+ years in data governance, data strategy, analytics, or data engineering leadership roles, including enterprise-scale data programs
• Proven track record of building data governance frameworks covering metadata, lineage, quality, and ethics
• Strong fluency in modern AI and data technologies including LLMs, generative AI, ML workflows, and data platforms
• Ability to translate strategy into measurable business value with scorecards, KPIs, and ROI models
• Experience running maturity assessments or diagnostic frameworks and creating actionable improvement plans
• Hands-on experience selecting and implementing data tooling across multiple teams
• Awareness of AI and data vendor and open-source landscapes
• Excellent communication skills for aligning engineers, sector leaders, and executives
• Strong stakeholder management in a large, matrixed organization
• Fluent business English (C1+); Polish is an advantage
• Comfortable working on-site/hybrid from Kraków and collaborating with distributed teams

Nice to have:

• Experience with data ethics, privacy, and AI regulatory compliance (e.g., GDPR, EU AI Act)
• Familiarity with metadata management, data catalog, and data quality tooling
• Background in management consulting, transformation programs, or internal innovation functions

• Own unified AI data governance and strategy across Comarch, including cross-sector data integration, metadata management, and data ethics
• Own the AI scorecard on a monthly and quarterly cadence, linked to MBOs, translating AI strategy into measurable business value across sectors
• Run the annual AI maturity diagnosis (McKinsey 12-practice framework) and track progress across the four dimensions of success
• Track ROI per sector against Hub running cost and publish the AI Impact Report
• Own AI data tooling and its technical implementation across sectors
• Define data standards, taxonomies, and metadata practices for cross-sector reuse and well-governed AI agents
• Establish and enforce data ethics, privacy, and compliance guardrails for AI across the group
• Partner with sector Heads of AI and CTOs to integrate data pipelines and ensure consistent, governed data foundations
• Define KPIs and reporting cadence; advise the Chief AI Officer and leadership on AI value realization and investment decisions
• Report scorecard results, ROI, and maturity to the Chief AI Officer and sector stakeholders