Business Intelligence Analyst MEA

Overview

Position Summary

QBI Process

• Lead the weekly forecasting process to ensure all respective franchise teams provide analysis and insights on expected performance and landing for the month and full quarter.
• Identify risks and opportunities to support market execution in line with financial targets.
• Lead the maintenance of data pipelines that enable an advanced and automated Quick Business Intelligence process and integrate with daily sales.

Market Intelligence

• Lead the in-market sales cadence: collect inputs from distributors, validate data, process and analyze, generate insights, and run action registers through to closure.
• Develop market and competitor analytics including market sizing, share, price/mix, and elasticity; generate insight packs for leadership and country commercial teams.
• Analyze business performance and growth drivers including pricing, portfolio, customer health, distributor stock risk, corrective actions and benefit tracking.
• Support forecasting and scenario planning: baseline and what-if analyses, assumptions, sensitivities, and driver narratives.
• Drive adoption of Hubble and dashboard tools for distributors, prioritize enhancements, create templates and glossaries, run trainings, and maintain feedback loops.
• Analyze market trends and opportunities, including digital and artificial intelligence use cases, to prioritize high-impact MedTech initiatives.

Data Analysis, Automation, and Reporting

• Partner with Commercial and Finance to frame hypotheses; define KPIs and semantic definitions; perform analyses and scenario modeling; present findings as required.
• Develop comprehensive reports and presentations that transform complex data into clear, actionable insights.
• Promote self-service analytics through improved dashboards and reports to enhance collaboration across the organization.
• Develop reusable data assets that address stakeholder demand across functions.
• Lead the design and implementation of data pipelines that enable advanced and automated business intelligence processes and integrate with other systems.

Dashboard Development

• Develop dashboards to meet business needs, own the technical plan (data readiness, refresh service level objectives, contingency) and participate in business readouts; quantify impact of releases.
• Optimize compute/storage, refresh performance, capacity planning, and automate dashboard refresh and analysis within budget guardrails.

Main Performance Measures

• QBI processes executed with excellence; stakeholder follow-ups finalized on time.
• Cadence outputs are decision-ready, timely, and actionable for leadership.
• Dashboards are adopted and trusted by intended audiences; feedback used to iterate.
• Data issues are triaged with clear narratives and owners; definitions remain stable and documented.

Required Skills

• Productivity: Advanced Microsoft 365 skills, particularly PowerPoint and Excel.
• BI & Visualization: Power BI (dataset optimization, data modeling, query building); familiarity with Tableau or QlikView.
• Market Intelligence: Experience with distributor analytics, forecasting fundamentals, and market research/survey tools.
• Business & Reporting: Business partnering experience and KPI design capability.
• Security & Governance: Knowledge of security-by-design practices for data solutions.
• Strong commercial acumen and understanding of pricing and go-to-market strategies.
• Advanced analytical skills with ability to synthesize data into strategic direction and executable plans.
• Excellent communication and stakeholder influence skills within a matrix organization.
• Problem-solving orientation, strategic mindset, and ability to operate in a fast-paced environment.
• High attention to detail, rigor, discipline, and strong ownership.

Preferred Skills

• Programming & Automation: SQL (intermediate to advanced), Python (pandas, PySpark), scripting and workflow automation.
• Data engineering familiarity: Data warehouse concepts, dbt basics, Power Automate for approvals/alerts, Git discipline.
• Analytics & Machine Learning: Statistical analysis, forecasting methods, and exposure to ML algorithms (classification, regression, clustering, NLP).