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Senior Analyst with 4+ years in SQL, Power BI & MIS Solutions
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Assessing your cultural and operational fit
Data Analyst with 4+ years at Tata Consultancy Services delivering SQL-driven reporting, Power BI dashboards, and MIS solutions across datasets of 10,000-50,000+ records. Skilled in optimized SQL (joins, window functions, CTEs), Power BI (DAX, data modeling), and Excel (Pivot Tables, Power Query) for end-to-end analytical workflows. Proven ability to automate reporting processes, validate data accuracy, and translate business requirements into structured, decision-ready outputs.
Saranathan College of Engineering, Trichy
B.E. Mechanical Engineering · Mechanical Engineering
August 1, 2017 – June 30, 2021
Tata Consultancy Services (TCS)
Data Analyst (System Engineer)
December 1, 2021 – Present
Bengaluru, Karnataka, India
Cheers Interactive Pvt. Ltd.
Analyst Intern (Data Reporting & Validation)
June 1, 2021 – November 30, 2021
Mumbai, Maharashtra, India
Layoff Trend Analysis
January 1, 2023 – December 31, 2023
Cleaned and analysed 3,000+ global layoff records using CTEs, window functions (DENSE_RANK, running totals), and correlated subqueries - found Tech and Finance sectors drove 60%+ of 2022-23 layoffs. Built staging table approach for data cleaning - removed duplicates using ROW_NUMBER, standardised text fields, handled NULL values, converted date formats.
E-Commerce Sales Performance Dashboard
January 1, 2023 – December 31, 2023
Built a 4-page Power BI dashboard on 9,694 e-commerce records using star-schema data model with dedicated Date Table and 8 DAX measures including MTD, YTD, and YoY time intelligence. Identified Tables and Bookcases sub-categories generating negative profit (-₹17.7K, -₹3.5K) due to 26% and 21% average discounts - drill-through navigation enabled root cause investigation. Surfaced seasonal sales pattern across all 4 years and found 155 of 793 customers (19.5%) generating negative margin using custom FILTER + CALCULATE DAX measure.
IBM HR Attrition Analysis
January 1, 2023 – December 31, 2023
Performed full EDA on 1,470 employee records across 35 variables using Pandas groupby, pd.cut() banding, and Seaborn visualisations to identify attrition drivers. Found overtime as #1 attrition predictor - 30.53% attrition rate for overtime workers vs 10.44% for non-overtime workers (3x difference). Identified at-risk employee profile: 18-25 age group (34.78% attrition), Sales department (20.63%), income below ₹4,787/month - enabling targeted HR intervention.
Cultural Fit Analysis
The candidate's project diversity, ranging from e-commerce performance to HR attrition and layoff trends, shows adaptability and a broad interest in applying data analysis across different domains. Their current role as a Data Analyst aligns well with the target role of Senior Analyst, indicating a clear career trajectory and commitment to the field. The breadth of skills across SQL, Power BI, Excel, and Python also suggests a versatile and continuous learning mindset, which is a good cultural fit for dynamic analytical environments.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through identifying root causes of negative profit and attrition drivers. Their experience in collaborating with cross-functional teams for requirements gathering indicates good teamwork and communication. The focus on reducing manual effort and improving efficiency suggests a proactive and optimization-oriented work attitude.