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Senior Analyst with 2+ years in SQL, Python, and Power BI
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Data Analyst with nearly 3 years of enterprise experience delivering data-driven business insights. Strong expertise in SQL, Python, and Power BI to build automated reporting systems, execute customer segmentation and model relational databases. Demonstrated analytical and problem-solving abilities in translating complex data trends into clear, actionable executive dashboards that bridge the gap between technical infrastructure and business strategy.
K.K. Wagh Institute of Engineering and Research
Electronics and Telecommunication Engineering · Electronics and Telecommunication Engineering
August 1, 2017 – June 30, 2021
Capgemini
Senior Analyst
September 1, 2021 – April 1, 2024
Mumbai, Maharashtra, India
Customer Shopping Behaviour Analysis
June 25, 2026 – Present
Built a complete end-to-end data pipeline on a retail dataset, covering customer demographics, purchase patterns and shipping behaviour using Excel as the data source Cleaned the data using Python (Pandas), imputed 37 missing review ratings with category-wise median, standardized column names, engineered an age_group feature via quantile binning, mapped purchase frequency to numeric values and dropped a fully redundant column after programmatic validation. Loaded the cleaned data into MySQL via SQLAlchemy and executed business driven queries using CTEs, window functions (dense_rank, sum over) and subqueries, covering revenue by gender, customer segmentation, top products per category and subscription vs. spend analysis. Built an interactive Power BI dashboard with KPI cards, revenue and sales breakdowns by category and age group and slicers for gender, subscription status and shipping type, surfacing 'Clothing' as the top revenue category ($104K) and 'Young Adults' as the highest spending age group ($62K).
View ProjectRetail Analytics
June 25, 2026 – Present
Designed and executed an end-to-end SQL data cleaning pipeline on 5,002 transaction records, eliminating duplicates via row_number(), correcting price anomalies through join-based updates. Performed category-level revenue analysis across 200 products, identifying 'Clothing' as the top performing segment at 6.7L, contributing nearly 55% of total 12.1L in revenue. Segmented 1,000 customers into value tiers using purchase frequency and spend thresholds. Identifying 18 high-value accounts and 130 low-engagement buyers to prioritize retention efforts.
View ProjectE-commerce Diagnostic
June 25, 2026 – Present
Engineered multi-table SQL queries across 4 tables using join, CTEs and subqueries to extract actionable customer behaviour and product performance insights. Tracked revenue trends and Average Order Value (AOV) using lag() window functions, identifying month-over-month growth patterns. Revealed 76% of customers as one-time or occasional buyers through purchase frequency segmentation, enabling targeted conversion and retention strategies.
View ProjectNetflix Content Dashboard
June 25, 2026 – Present
Modelled 5,283 Netflix titles (3,407 Movies, 1,876 Shows) spanning across 1953 to 2022 using power query for transformation and DAX for custom measures, including Weighted IMDb Score, reliability classification (votes ≥1,000), audience grouping and a popularity index. Developed an interactive dashboard covering catalogue overview, content quality, audience segmentation and timely insights with dynamic slicers for content type, age certification and release year. Surfaced key insights: Teen rated content led quality at avg IMDb 7.0 vs Kids at 6.4; long-form shows outperformed movies (7.9 vs 6.7); catalogue production peaked 2018-2020 before a sharp decline.
View ProjectSuperstore Sales Dashboard
June 25, 2026 – Present
Analysed $2.30M in sales and $286.40K profit across 3 categories, 3 customer segments and 49 states over 4 years using DAX measures for revenue, profit margin and KPIs. Built a dashboard with category drill-down, sub-category scatter analysis and state-level geo mapping. Identified 'Consumer' as the dominant segment at $1.16M (50.56% of revenue) with 'Technology' showing the strongest profit growth trend from 2018 to 2021.
View ProjectAZ-900: Microsoft Azure Fundamentals
Microsoft Azure
June 1, 2026 – Present
Data Analytics Job Simulation
Deloitte (Forage)
June 1, 2026 – Present
SQL for Data Analysis
Coding Ninjas
June 1, 2026 – Present
Data Visualization with Power BI for Data Analysis
Coding Ninjas
June 1, 2026 – Present
Associate Reactive Developer
OutSystems
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from e-commerce to Netflix content analysis and retail analytics, indicates adaptability and a broad interest in applying data skills across different domains. Their professional experience at Capgemini, working on projects like Network Rail and Sales Performance, shows experience in a corporate environment. The certifications and personal projects align well with a data-driven culture that values continuous learning and practical application.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving abilities, translating complex data trends into clear, actionable insights. Their experience in automating data processes suggests an operational efficiency mindset. The project descriptions indicate a focus on delivering business value through data.