Data Analyst Power Bi

Vinod Kumar Mannava Data Analyst

*****@***********.*** 763-***-**** USA LinkedIn

Summary

Detail-oriented and highly analytical Data Analyst with 5+ years of extensive experience in collecting, processing, and analyzing complex datasets. Proficient in utilizing tools like SQL, Python, Tableau, and Power BI to generate actionable insights and drive data-driven decision- making. Expertise in data visualization, statistical modeling, and forecasting techniques to support business objectives. Strong background in ensuring data quality, implementing automation, and delivering reports that enhance operational efficiency and business strategy. Committed to providing accurate, reliable, and impactful analysis to stakeholders.

Technical Skills

Data Analysis & Visualization: Power BI, Tableau, Excel

Programming: Python (Pandas, NumPy, Requests, BeautifulSoup, SciPy), R (ARIMA)

Database Management: SQL (Advanced SQL, JOINs, Window Functions, GROUP BY), AWS S3

Data Processing & Automation: Python (Data cleaning, time-series analysis), AWS Lambda

Statistical & Forecasting Techniques: Time-Series Analysis, Statistical Modelling, ARIMA, Regression Analysis

Data Validation & Quality: DAX, Data Validation, LOD (Level of Detail) Calculations

Cloud Tools & Technologies: AWS Lambda, S3, Azure Data Factory, Azure SQL Database, Excel, Tableau, Power BI

Professional Experience

Data Analyst, Barclays 02/2024 – Present Remote, USA

Worked on Multi-Channel Customer Journey Analysis and Optimization at Barclays USA, collaborating with cross-functional teams to analyze over 5 million touchpoints across mobile apps, web, call centers, and physical branches for optimization.

Utilized SQL to extract, join, and manipulate customer interaction data from AWS S3 storage. Applied advanced techniques such as CTEs and Window Functions for efficient data aggregation, reducing data handling time by 40% and optimizing analysis processes.

Applied Python libraries such as Pandas, NumPy, Requests, and BeautifulSoup for data cleaning, preprocessing, and web scraping. Automated data extraction processes, handling over 10 million rows, reducing manual processing time by 40%.

Conducted funnel and path analysis on customer behavior, identifying significant drop-off points and optimizing the journey. Proposed improvements that resulted in an 18% increase in online conversions and 20% higher mobile app sign-ups.

Ensured data quality and consistency by implementing Power BI for Data Validation using DAX formulas. Validated over 50 metrics from various sources, improving report reliability and accuracy by 30% for actionable insights.

Developed and managed interactive Power BI dashboards to visualize customer journeys. Leveraged features like Drill-through and Calculated Columns to deliver real-time insights, driving a 15% increase in customer retention and data-driven decision-making.

Data Analyst, Standard Chartered Bank 03/2020 – 07/2022 Hyderabad, India

Collected and integrated large datasets for Market Trend Analysis of banking products, including customer acquisition, product usage, and market trends, from internal databases and external sources. Improved analysis efficiency by 20%, aiding faster decision-making.

Used SQL to extract and manipulate large volumes of banking product data. Applied advanced techniques such as JOINs and Window Functions to aggregate product usage statistics and identify emerging market trends, reducing reporting time by 30%.

Directed in-depth market trend analysis to identify key growth areas for banking products. Performed time-series analysis and applied statistical methods to forecast demand for savings accounts and credit cards, enhancing strategic decision-making.

Utilized Python libraries like Pandas, NumPy, and SciPy to clean, preprocess, and analyze data. Automated data cleaning tasks, reducing processing time by 40%, enabling faster insights for product development teams.

Applied R's ARIMA method for time-series forecasting, analyzing market demand for loans and credit products. Provided insights into seasonal trends, which helped optimize promotional campaigns and resulted in a 15% increase in loan product performance.

Ensured data accuracy through rigorous validation procedures in Excel, using Data Validation and LOD (Level of Detail) calculations to ensure high-quality analysis for senior management, improving reporting reliability by 25%.

Built dynamic Tableau dashboards to visualize market trends and customer preferences across banking products. Leveraged advanced features such as Parameters and Calculated Fields, improving decision-making and reducing reporting lead time by 25%.

Used Azure Data Factory for data integration and Azure SQL Database for efficient data storage and management, improving the scalability and performance of data analysis workflows.

Associate Data Analyst, S&P Global 01/2018 – 02/2020 Hyderabad, India

Analyzed historical sales data, market trends, and customer demographics to predict demand for consumer products. Developed insights that informed inventory planning, ensuring efficient production schedules and timely availability of popular products.

Utilized SQL to extract and query data from large relational databases. Applied JOINs and GROUP BY clauses to aggregate product sales data across regions, improving the accuracy of demand forecasts by 18%.

Employed Python and its libraries such as Pandas, NumPy, and SciPy to process and clean large datasets. Used time-series analysis techniques to identify trends, provide more precise forecasts and reducing prediction errors by 22%.

Created detailed Tableau dashboards to visualize sales patterns, demand forecasts, and product performance metrics. Delivered real- time insights to senior management, enabling quicker decision-making and enhancing the responsiveness of inventory management.

Utilized Excel for data validation and analysis, performing in-depth pivot table analysis and creating forecasting models. Improved report accuracy and reduced manual data processing time by 30%, streamlining demand planning processes.

Education

Master of Science in Information Technology and Management

Concordia University, Saint Paul 08/2022 – 12/2023 Minnesota, USA

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