Senior Manager Quality Data Analytics

Role Purpose

Serves as the analytics subject matter expert for Quality & Continuous Improvement across EMEA, owning the design, development, and evolution of the EMEA-wide performance analytics architecture. Translates complex operational data into strategic insights that drive decision-making at senior leadership level. Acts as the bridge between business stakeholders and data — defining what to measure, how to measure it, and what it means.

Key Responsibilities

Analytics Architecture & Strategy

• Design and own the EMEA Quality & CI KPI architecture — defining metrics, hierarchies, calculation logic, and data lineage across all Business Units


• Establish standardised KPI definitions (Customer Claims, FPY, COPQ, Warranty, VoC, Supplier Quality) to enable consistent EMEA-wide comparison


• Define the analytics roadmap for the function, identifying where advanced analytics, process mining, and AI-enabled approaches can add value


• Architect the data model underpinning EMEA Quality & CI reporting — ensuring scalability, accuracy, and maintainability

Insight Development & Stakeholder Advisory

• Develop and present monthly operating review analytics to senior leadership — translating data into narrative, identifying root causes, and recommending actions


• Partner with BU Quality leaders to define leading indicators and predictive analytics that shift the function from reactive to proactive


• Conduct deep-dive analyses on quality trends, warranty patterns, and supplier performance to surface systemic issues


• Provide data-driven recommendations to Senior Director and VP-level stakeholders on performance improvement priorities

Platform & Capability Development

• Design and build Power BI dashboards and reports that serve as the single source of truth for EMEA Quality & CI performance


• Define requirements for data pipelines and integrations (SAP, Snowflake, other source systems)


• Evaluate and implement process mining tools (Celonis or equivalent) to identify inefficiencies in quality processes


• Drive automation of manual reporting — target 50% reduction in manual effort within 12 months


• Develop reusable templates, calculation logic, and self-service analytics capabilities for the broader Quality team

Governance & Data Quality

• Own the data governance framework for Quality & CI analytics — defining data ownership, refresh cadences, validation rules, and exception handling


• Establish and maintain data quality standards; monitor and resolve discrepancies across source systems


• Lead 8D maturity tracking methodology — defining scoring criteria and reporting dashboards

Required Skills & Experience

Technical (Must-Have)

• Expert-level Power BI development (complex DAX, data modelling, row-level security, deployment pipelines)


• Advanced SQL (query optimisation, stored procedures, complex joins across large datasets)


• Data modelling and warehouse concepts (star schema, fact/dimension design)


• Experience with process mining platforms (Celonis preferred)


• Working knowledge of SAP data structures (QM, MM, SD modules)

Technical (Preferred)

• Power Platform (Power Automate, Power Apps) for workflow automation


• Python or R for statistical analysis


• Experience with Snowflake or similar cloud data platforms


• AI/ML-enabled analytics (anomaly detection, predictive quality)

Functional

• 10–15 years of experience in data analytics, business intelligence, or performance management


• Demonstrated experience designing KPI frameworks and analytics architectures from scratch


• Experience presenting to and advising senior leadership (Director+ level)


• Understanding of Quality management systems, manufacturing KPIs, and operational excellence principles


• Experience working in global matrix organisations across multiple time zones

Behavioral

• Strategic thinking — ability to see the "so what" behind the data and connect analytics to business outcomes


• Stakeholder management — comfortable challenging senior leaders with data and influencing without authority


• Ownership mindset — treats the analytics platform as their product, drives quality and continuous improvement unprompted


• Structured communication — can distil complex analysis into clear, actionable narratives for non-technical audiences

Education

Bachelor's degree in Engineering, Data Science, Statistics, Computer Science, or Business (required)

Master's degree or MBA (preferred)

Success Measures (First 12 Months)

• Single EMEA Quality & CI dashboard live and adopted by all BUs


• Standardised KPI definitions agreed and implemented across EMEA


• 50% reduction in manual reporting effort


• Monthly operating review analytics cadence established


• 8D maturity tracking methodology defined and dashboard deployed


• Process mining pilot completed for at least one quality process