Mani Varma
Summary
Detail-oriented Data Analyst with 2+ years of experience in the Finance and Banking sector, specializing in translating complex financial datasets into clear, actionable insights for senior stakeholders. Expert in advanced SQL, Power BI, Tableau, and Excel/financial modeling, with a strong track record of streamlining reporting processes, supporting regulatory compliance, and driving data-backed decision-making across credit, risk, and operations teams. Master's-level analytical rigor applied to real-world financial challenges.
Overview
2
2
years of professional experience
Work History
Data Analyst
American Express
06.2024 - Current
- Developed 20+ Power Bl dashboards tracking loan portfolio health, delinquency rates, and covenant compliance, giving credit officers real-time visibility and reducing manual reporting by 45%.
- Authored advanced SQL queries (window functions, CTEs, stored procedures) against a 150M-row
Oracle data warehouse to produce daily risk exposure reports consumed by the Chief Risk Officer. - Built a dynamic Excel financial model integrating live SQL data feeds to stress-test credit portfolios under multiple macroeconomic scenarios, directly supporting quarterly ALCO committee presentations.
- Automated month-end regulatory reporting (Call Report schedules) using SQL and Excel macros, cutting preparation time from 3 days to 4 hours and eliminating manual reconciliation errors.
- Partnered with the Compliance team to design data validation frameworks ensuring 100% accuracy across Basel III capital adequacy calculations submitted to federal regulators.
- Delivered ad-hoc executive analyses on interest rate sensitivity, deposit concentration risk, and customer profitability, cited in three board-level strategy meetings.
- Analyzed customer data to identify trends and insights for marketing strategies.
- Developed interactive dashboards using Tableau for real-time data visualization.
- Conducted statistical analyses to support decision-making across departments.
- Prepared comprehensive reports detailing findings and recommendations for stakeholders.
- Ensured data integrity by validating information from various sources.
- Trained team members on best practices for data management and analysis.
- Translated raw data into meaningful information using statistical techniques.
- Leveraged SQL queries to extract, transform and load data into databases.
- Assisted with efforts to track, evaluate and report on impact of programs using multiple data sources.
- Conducted workshops and training sessions for non-technical staff on data analytics and data-driven decision-making.
- Filtered and cleaned data, and reviewed computer reports, printouts, and performance indicators to locate and correct code problems.
- Collaborated with IT and business process owners to enhance system requirements for analytical purposes.
- Developed and implemented data collection systems and strategies to optimize statistical efficiency and data quality.
Data Analyst Intern
American Express
01.2024 - 06.2024
- Built Tableau dashboards for branch performance, product cross-sell rates, and customer attrition, adopted by 12 regional managers as their primary reporting tool.
- Wrote complex SQL queries to segment 500K+ retail customers by profitability tier, enabling targeted marketing campaigns that improved cross-sell conversion by 18%.
- Maintained and enhanced Excel-based MIS reporting suite (30+ reports), applying BA automation to reduce monthly report generation time by 60%.
- Supported the operations team with root-cause analysis on transaction processing failures, identifying a data pipeline gap that was costing the bank $120K/year in manual corrections.
- Supported the development of dashboards to track key performance metrics effectively.
- Conducted data quality checks to ensure accuracy and consistency in datasets.
- Participated in meetings to discuss findings and provide input on future initiatives.
- Researched industry standards and best practices to enhance analytical approaches used.
- Documented procedures for data handling and reporting to streamline workflows across teams.
- Performed exploratory data analysis on large datasets to identify trends and outliers.
Education
Master of Science -
Lewis University
UNITED STATES05-2024
Bachelor of Science -
ICFAI UNIVERSITY
INDIA05-2022
Skills
- SQL (Advanced)
→ Joins, CTEs, Window Functions, Aggregations - Python (Data Analysis)
→ Pandas, NumPy, Matplotlib, Seaborn - Excel (Advanced)
→ Pivot Tables, XLOOKUP, Power Query - SQL querying
- Data visualization
- Dashboard development
- Statistical analysis
- Data integrity
- Financial modeling
PROJECTS
1. Transaction Risk & Spending Pattern Analysis
- Analyzed large-scale transaction datasets to identify spending patterns, anomalies, and risk indicators
- Segmented customers based on transaction behavior
- Detected unusual activity using statistical thresholds
Tools: SQL + Python (Pandas) + Power BI
- Analyzed 100K+ financial transactions to identify spending trends and anomalous patterns using SQL and Python
- Developed risk-based segmentation logic to flag high-risk transactions, improving anomaly detection accuracy
- Built Power BI dashboard to monitor transaction behavior, customer segments, and risk indicators
- Reduced manual analysis effort by automating data processing workflows
2. Customer Retention & Churn Analytics (Credit Card Domain)
- Identified customers likely to stop using credit cards
- Analyzed behavioral patterns (spend drop, inactivity, payment delays)
- Built insights to improve retention strategies
Tools: Python + SQL + Tableau
- Performed churn analysis on customer transaction data to identify key drivers of attrition
- Built predictive insights using Python (Pandas) to detect early churn signals
- Created Tableau dashboards to visualize customer lifecycle and retention trends
- Delivered actionable insights to improve customer retention strategies
3. Fraud Detection Exploratory Analysis
- Analyzed fraudulent vs normal transactions
- Identified patterns (location mismatch, high-value spikes)
- Created fraud indicators
Tools: Python + SQL
- Conducted exploratory data analysis on transaction datasets to identify fraud patterns and anomalies
- Applied statistical techniques to distinguish fraudulent behavior from normal transaction activity
- Generated insights to enhance fraud detection strategies
Timeline
Data Analyst
American Express
06.2024 - Current
Data Analyst Intern
American Express
01.2024 - 06.2024
Master of Science -
Lewis University
Bachelor of Science -
ICFAI UNIVERSITY