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Pfizer Logo Pfizer · IT - Administration

Reporting & Diagnostic Analytics Manager

Costa Rica, Escazu · Job Posted February 20, 2026
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Job Description

The Reporting & Diagnostic Analytics Manager designs and scales market-ready reporting and diagnostic analytics solutions that strengthen commercial excellence and enable data-driven decision making across global markets. This role owns the end-to-end delivery of descriptive and diagnostic analytics assets—from discovery and data assessment through design, build, deployment, rollout, and continuous improvement—leveraging solution vehicles such as dashboards, automated alerts, and self-serve analytics. By applying deep BI expertise and heuristic/rules-based logic to translate data into clear insights and recommendations, this role equips commercial stakeholders to monitor performance, diagnose drivers, and take timely actions with measurable impact.

Job Responsibility

  • Discover & Scope: Partner with commercial decision makers to identify priority reporting and diagnostic needs
  • KPI Framework & Data Readiness: Define KPI frameworks and metric governance (definitions, calculation logic, data dictionaries), and lead early data assessments to confirm data availability, quality, and consistency across markets
  • Design & Deliver Analytics Assets: Own the design and end-to-end delivery of scalable descriptive/diagnostic solutions
  • Develop Rules-Based Insight & Alerting Logic: Build and maintain heuristic/rules-based engines (thresholds, triggers, exception rules, segmentation logic) to generate diagnostic insights, performance flags, and actionable recommendations
  • Vendor Delivery Leadership & Quality Assurance: Lead vendor execution by translating business needs into requirements and acceptance criteria, steering build/test iterations, coordinating UAT, and ensuring deliverables meet quality, performance, security, and business usability standards
  • Package Recommendations: Convert insights into actionable, ready-to-use commercial recommendations
  • Deploy, Enable & Drive Adoption: Manage deployment and rollout to markets, including enablement materials (user guides, metric definitions, training), and establish feedback mechanisms to support sustained adoption
  • Monitor & Improve: Monitor asset performance and usage post-deployment, address defects and enhancement opportunities, refine insight logic and visualizations, and drive continuous improvements through a prioritized backlog
  • Track Value: Measure and report the business value and effectiveness of reporting/diagnostic assets (e.g., adoption, time-to-insight, decision support outcomes), and use insights to prioritize enhancements

Requirements

  • Bachelor’s degree in Business Analytics, Information Systems, Data Science, Statistics, Industrial Engineering, Computer Science, or a related quantitative/technical field
  • Minimum of 3 years of experience in BI, reporting, descriptive/diagnostic analytics, or commercial analytics roles delivering analytics solutions that support business decisions
  • Advanced proficiency in Python, SQL, including data extraction, joins, transformations, and query optimization
  • experience working with structured relational datasets
  • Deep expertise in BI and visualization tools, with strong hands-on experience in Tableau (or equivalent enterprise BI platform)
  • Experience translating complex data into clear insights, diagnostic narratives, and actionable recommendations for non-technical stakeholders
  • Strong vendor and stakeholder management capabilities, including the ability to influence without formal authority, drive alignment, and manage delivery through partners
  • Strong communication and interpersonal skills, including the ability to present insights clearly to senior commercial stakeholders
  • Advanced Microsoft Excel skills (data modeling, pivot tables, complex formulas, analysis)
  • Fluent in English (written and spoken) — minimum C1 level required

Nice to have

  • Experience working in the pharmaceutical / life sciences industry
  • Experience working with modern data platforms (cloud data warehouses, ETL/ELT concepts) and collaborating with data engineering teams
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