Senior Data & Analytics Specialist

Job Description:

Responsbilities

•   Coordinate and manage data deliveries from self-service analytics (SSA) platforms and external vendors — ensuring timeliness, completeness, and format compliance — while monitoring feeds proactively to identify gaps, anomalies, or drift before downstream impact. 
•  Maintain and update business rules, mapping logic, taxonomies, and classification standards — ensuring they reflect current market realities and are structured for both human and machine consumption. 
•  Troubleshoot data issues end-to-end — from detection through root cause analysis to corrective action — driving remediation with urgency and documenting resolutions for future prevention. 
•  Support analytics and BI reporting requests — preparing data, validating outputs, maintaining Power BI dashboards, and ensuring reports reflect accurate, timely, and contextually correct information with appropriate business interpretation. 
•  Facilitate local market data requests and ad-hoc analytical needs — acting as the bridge between market teams, global data platforms, and cross-functional stakeholders. 
•  Identify opportunities to transition recurring manual requests into self-service reporting or automated delivery. 
•  Prepare and structure data for AI use cases — ensuring datasets are clean, well-labeled, semantically tagged, and documented with the context AI models need to interpret them accurately, with explicit consideration for how attributes will be consumed by AI models. 
•  Support the operationalization of AI products — assisting with data validation, output testing, feedback loops, and continuous improvement of AI-generated insights. 
•  Actively leverage AI tools (e.g., Copilot, GenAI assistants, n8n, automation scripts) in daily work to increase efficiency, reduce manual effort, and improve output quality — serving as a role model for AI-augmented operations. 
•  Identify and implement automation opportunities within data operations — proposing where AI or rule-based automation can replace repetitive manual tasks in mapping, validation, and quality checks. 

Qualification & Experience

•  Bachelor's degree in Business, Economics, Data Analytics, Information Management, or related field. 
•  2–5 years of experience in data operations, data quality management, business analytics support, or commercial data analytics — ideally in FMCG, Consumer Health, or Pharmaceuticals. 
•  Hands-on experience with syndicated market data (IQVIA, NielsenIQ, GfK) — including familiarity with data structures, delivery formats, and common quality issues — is a plus. 
•  Experience defining and applying data mapping rules, product matching logic, and classification standards.