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Data Analyst with 1+ years in SQL, Python & Power BI
Engineering graduate with hands-on experience analyzing large datasets, building KPI dashboards, and delivering data-driven insights to business stakeholders across multiple industries. At Temenos, built and maintained Power BI and Salesforce dashboards tracking operational and commercial KPIs for teams across APAC, EMEA, and US regions, ran data quality audits to ensure reporting accuracy, and translated analysis findings into structured recommendations for Sales and Operations teams. At Wells Fargo, applied Python and SQL to analyze large financial datasets, identified trends and anomalies, and produced structured reports for operational decision-making. At TGTRANSCO, built statistical demand forecasting models using historical operational data and documented findings for senior planning stakeholders. Proficient in Python (pandas, numpy), SQL, Excel (Advanced), and Power BI. Familiar with Tableau concepts and actively uses AI tools including ChatGPT to accelerate analysis and reporting tasks.
University College of Engineering, Osmania University
Bachelor of Engineering · Electrical and Electronics Engineering
August 1, 2021 – June 30, 2025
Temenos
Data Analyst Intern, Commercial Operations
July 1, 2025 – February 1, 2026
Hyderābād, Telangana, India
Wells Fargo
Financial Data Analyst Intern (Virtual)
January 1, 2024 – December 31, 2024
India
TGTRANSCO
Research Analyst Intern
January 1, 2024 – December 31, 2024
Hyderābād, Telangana, India
Retail Sales Performance Analyzer
January 1, 2025 – June 1, 2026
Built an end-to-end retail analytics pipeline using a publicly available sales dataset (Kaggle superstore data). Used Python (pandas) to clean and transform raw transaction records, SQL to store and query sales data by region, category, and time period, and Power BI to build interactive dashboards tracking revenue trends, product category performance, regional KPIs, and customer segment profitability. Applied statistical analysis to identify top and bottom performing product lines, seasonal demand patterns, and discount impact on margins. Produced an executive summary report presenting findings and actionable recommendations in plain language for a non-technical retail management audience.
Automated Data Pipeline with Trend Monitoring and Alerting
January 1, 2025 – June 1, 2026
Built a Python pipeline that collects live data via REST API, applies validation and deduplication rules, runs statistical threshold checks to detect anomalies, and logs flagged records to a SQL database with automated alerts. Designed to demonstrate end-to-end data accuracy enforcement and proactive trend monitoring across a continuously updating dataset.
Cultural Fit Analysis
The candidate's experience across different industries (retail, finance, power infrastructure) and types of projects (sales performance, financial analysis, demand forecasting) indicates a broad interest and adaptability, which contributes positively to cultural fit. Their proactive use of AI tools also suggests a forward-thinking and efficiency-driven mindset. However, the experience is primarily internship-based, which might require more mentorship in a full-time senior role.
Soft Skills & Operational Fit
The candidate demonstrates strong communication skills through their ability to present findings to diverse audiences and translate technical observations into business recommendations. Their project descriptions highlight a structured approach to problem-solving and an understanding of data quality, which are crucial for operational fit. The use of AI tools like ChatGPT for analysis acceleration suggests an adaptive and efficient work attitude.