Requirements

Expected technologies

Jira

Confluence

Microsoft Azure

Data Lake

Microsoft Power BI

Optional technologies

Azure ML

Data Bricks

Our requirements

• 10+ years of experience in data-driven product or platform roles, with 5+ years specifically in data platform or data lake environments.
• Proven track record managing complex enterprise-scale data platforms using Microsoft Azure, Databricks, and related tools.
• Strong understanding of supply chain and/or quality management business processes and data needs.
• Experience delivering AI-powered solutions or advanced analytics within data products.
• Demonstrated ability to lead Agile teams with an emphasis on accountability, urgency, and financial discipline.
• Proficiency in Agile product management and delivery tools: Jira, Aha!, Confluence, or similar.
• Deep familiarity with data governance, security standards, and enterprise architecture compliance.
• Outstanding communication and stakeholder management skills, with the ability to translate technical requirements into business value.

Optional

• Industry experience in manufacturing, life sciences, CPG, or global supply chain operations.
• Knowledge of modern architectural paradigms like data mesh, data fabric, or domain-oriented data ownership.
• Exposure to working with global, matrixed organizations with multiple stakeholder groups.

Your responsibilities

• Platform ownership: Serve as the end-to-end product owner for the Quality and Supply Chain Data Lake, managing its lifecycle, architecture compliance, and overall platform health.
• Roadmap development & execution: Define and drive the platform roadmap based on strategic priorities and business demand, balancing long-term vision with rapid, incremental delivery.
• Agile delivery with urgency: Operate in an agile methodology while maintaining strict accountability for timeliness and budget. Lead sprints, manage trade-offs, and remove blockers to ensure delivery success.
• AI & Advanced analytics enablement: Drive integration of AI/ML solutions and advanced analytics use cases that improve supply chain visibility, quality insights, and operational efficiency.
• Stakeholder management and cross-team collaboration: Work with a diverse mix of technical and business stakeholders, including data engineers, data analysts, business analysts, data architects, solutions architects, scrum masters, and domain experts across Quality and Supply Chain functions.
• Compliance & Governance: Ensure platform development adheres to enterprise reference architecture, cybersecurity standards, and data governance frameworks.
• Tooling & Delivery management: Use tools like Jira, Aha!, Confluence, or similar to manage backlog, sprints, roadmaps, and communications. Track KPIs and delivery metrics to continuously improve execution.
• Communication & Alignment: Clearly articulate priorities, trade-offs, value delivery, and technical implications to both executive stakeholders and implementation teams.