Mohammed Nafees N

Data Analyst | Business Analyst | Product Analyst

Talent

BengaluruMember since 10 June 2026

SQLPower BIPythonData ValidationPower AutomateGitJavaScriptHTMLCSS

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About

Senior Data Operations Analyst at NielsenIQ with 3+ years transforming large-scale FMCG, retail, and e-commerce data into decision-ready insights for global clients including Procter & Gamble, Coca-Cola, L'Oréal, Mondelez, and SC Johnson. Promoted for delivering automated Power BI dashboards and Power Automate workflows that cut analyst effort by 20–30%. Skilled in SQL, Python, Power BI, and stakeholder collaboration — with hands-on experience building internal web applications in React that replaced legacy Excel macro tools.

Experience

Senior Data Operations Analyst (promoted from Data Processing Analyst) at NielsenIQ (2022-10 – Present)

Promoted to Senior Analyst within 2 years, recognised for consistent delivery quality and measurable contributions to client deliverables.
Analysed and validated large FMCG datasets across retail and e-commerce channels, maintaining data accuracy above 98% in line with client business rules.
Served as primary data contact for global clients — P&G, Coca-Cola, L'Oréal, Mondelez, and SC Johnson — through NIQ's Discover platform, resolving data gaps and discrepancies that improved insight reliability.
Built and maintained automated data validation and monitoring workflows using Power BI and Power Automate, reducing manual effort and turnaround time by 20–30%.
Partnered with client business analysts and brand managers to identify trends, surface actionable KPIs, and support omnichannel market performance tracking.
Led process improvement and automation initiatives that enhanced operational efficiency and data quality across the team.
Managed and resolved client data tickets end-to-end using Microsoft Dynamics 365, maintaining ticket ownership from logging through to closure and ensuring SLA compliance for global FMCG accounts.
Investigated complex data issues reported by clients and internal teams, performing root-cause analysis across datasets, pipelines, and business rules to pinpoint and resolve discrepancies efficiently.
Solved recurring data quality issues by identifying underlying patterns, implementing validation rules, and documenting findings to prevent repeat occurrences and reduce ticket volume over time.