Analytics Enablement & Strategy Define and drive an enterprise analytics enablement strategy that improves analytical quality, consistency, and trust across Operating Companies and Sonepar Management Group. Establish clear expectations and best practices for analytical problem framing, metric definition, and solution design - independent of specific tools or platforms. Act as a strategic advisor to business and analytics leaders on when and how different analytics approaches and productivity tools should be used, based on business need and maturity. Partner closely with Data Governance to ensure analytics practices align to a unified governance model, including standards for metrics, documentation, reuse, and ownership. Leadership of Analytics Engineers (Data Services) Manage and develop analytics engineers within the centralized Data Services team, setting direction for how analytical solutions are designed, reviewed, and enabled across the organization. Promote modern analytics engineering practices, including modular design, reuse, documentation, and performance-aware modeling. Ensure analytics engineers are focused on enablement and leverage, not long-term ownership of bespoke reporting. Cross-Organizational Enablement & Collaboration With the Data Product Manager, support alignment between central Data Services and distributed Operating Company / Sonepar Management Group analysts, reducing fragmentation and duplicated effort. Support analyst communities by identifying common analytical patterns, shared needs, and opportunities for standardization or reuse. Influence without direct authority - driving alignment through clarity, shared frameworks, and strong analytical reasoning. Help shift the organization from reactive, ad-hoc reporting toward prioritized, outcome-driven analytics work. Tooling & Platform Perspective (Non-Operational) Provide strategic guidance on analytical usage of Power BI and the broader Microsoft analytics ecosystem. Bring informed perspective on modern analytics stacks from prior experience (e.g., SQL-based modeling, semantic layers, cloud data platforms), without requiring deep specialization in any single tool. Understand and advise on analytical use cases related to Sonepar’s in-house productivity applications, while not directly managing or delivering those solutions. Required Qualifications: Strong analytical problem-solving orientation, with the ability to translate ambiguous business questions into clear, structured analytical approaches and articulate tradeoffs and assumptions. 7 years of experience in analytics, analytics engineering, business intelligence, or a closely related field, including experience operating in complex, matrixed organizations. Demonstrated experience leading or influencing analytics engineers, analysts, or analytics communities through standards, coaching, and enablement. Proven exposure to modern analytics platforms and practices across multiple environments (e.g., cloud data platforms, semantic modeling, analytics engineering, BI tools), with the ability to apply best practices pragmatically. Strong understanding of analytics governance concepts, including metric consistency, reuse, documentation, data product ownership, and lifecycle management. Ability to operate effectively across centralized and federated analytics models, balancing autonomy with enterprise consistency. Excellent communication skills, with the ability to engage credibly with technical teams, business leaders, and senior executives. Comfort influencing without authority and navigating organizational ambiguity while maintaining focus on outcomes Understanding of modern analytics architecture concepts, including data transformation patterns, semantic/metrics layers, and analytical model design. Familiarity with analytics development best practices such as version control, peer review, and environment promotion, sufficient to guide standards and reviews. Preferred Experience: Experience working across multiple companies or industries, with exposure to varying levels of analytics maturity. Background in analytics engineering, data analytics, or business intelligence with strong grounding in technical best practices (not just report development). Experience supporting or enabling distributed analyst communities. Familiarity with Power BI and the Microsoft Fabric ecosystem, paired with openness and credibility across other modern analytics tools and approaches. Experience working with relational databases and analytical data stores, including writing and reviewing SQL and understanding performance considerations Working knowledge of Python for analytics, automation, and data transformation use cases. Foundational understanding of data engineering concepts, including data movement, transformations, and dependency management (hands-on expertise not required). What Success Looks Like: Analysts across Op. Cos and SMG spend more time on high-value, prioritized analytical work and less time recreating existing logic or reports. Analytics solutions are easier to understand, reuse, and trust - regardless of who built them or where they live. Data Services is viewed as an enablement and strategy partner, not a bottleneck or delivery factory. A clear, unified analytics governance model is actively reinforced through day-to-day analytical practice, not just documentation.