Data Analyst Big

Rishika Kanugula Data Analyst

*******.*********@*****.*** 386-***-**** USA LinkedIn

Summary

Results-driven Data Analyst/Engineer with 3+ years of hands-on experience in data extraction, transformation, and visualization across cloud platforms. Proficient in SQL, Python, R, and big data tools including Spark and Databricks. Skilled in building automated ETL pipelines, performing advanced analytics, and delivering actionable insights through interactive dashboards. Adept at working in Agile teams, ensuring data quality, and supporting business decisions through scalable, data-driven solutions. Technical Skills

• Programming & Scripting: Python (Pandas, NumPy, Dask, PySpark, Requests), R, SQL, Bash

• Data Engineering Tools: Apache Airflow, Apache Spark, Azure Data Factory, AWS (S3, Redshift), Azure Databricks

• Databases & Data Warehousing: AWS Redshift, Azure SQL Database, Snowflake, Google BigQuery

• ETL & Data Integration: ETL Pipelines, Data Wrangling, Data Validation, Data Quality Management, API Integration

• Data Visualization & BI Tools: Power BI (Drill-through, Bookmarks, Q&A), Tableau (LOD Expressions, Dynamic Filters), Excel (Pivot Tables, Macros, Conditional Formatting)

• Big Data & Cloud Platforms: AWS, Microsoft Azure, Databricks

• Analytics & Statistical Methods: Exploratory Data Analysis (EDA), RFM Analysis, Cohort Analysis, Churn Prediction, Survival Analysis

• Workflow & Methodologies: Agile (Scrum), Version Control (Git), Documentation & Reporting Professional Experience

Data Analyst, Deutsche Bank 01/2025 – Present Remote, USA

• Worked on Customer Segmentation and Behavior Analysis to enhance personalized banking services by analyzing Deutsche Bank’s U.S. customer data, collaborating with cross-functional teams using Agile methodology resulting in a 15% increase in targeted campaign effectiveness.

• Utilized SQL and advanced ETL techniques including window functions and CTEs to extract, transform, and load large datasets from AWS Redshift and AWS S3 data lakes and collaborated with data engineers to optimize data pipelines using Apache Airflow.

• Applied Python libraries such as Pandas, NumPy, Dask, and PySpark to preprocess, clean, and manipulate massive datasets leveraging PySpark for distributed computing on big data frameworks to efficiently handle scalable data processing.

• Conducted RFM (Recency, Frequency, Monetary) analysis identifying high-value customers which contributed to a 12% increase in cross-sell opportunities by prioritizing retention and upsell strategies.

• Performed rigorous data validation using Python scripts for anomaly detection and consistency checks and complemented efforts with Excel-based pivot tables and macros to ensure data accuracy across multiple sources.

• Developed advanced Power BI dashboards integrating drill-through, bookmarks, and AI-driven Q&A features to provide interactive visual insights and led reporting and documentation efforts resulting in 30% faster decision-making for management. Data Analyst, Cognizant 01/2021 – 08/2023 Hyderabad, India

• Identified key factors leading to 18% customer churn in Cognizant’s IT service contracts by collaborating with cross-functional teams and conducting detailed requirement gathering sessions to align business goals and data scope.

• Extracted and transformed data from Azure SQL Database using advanced SQL techniques like window functions and common table expressions. Integrated data pipelines with Azure Data Factory for automated ETL workflows on large-scale client datasets.

• Conducted cohort analysis and trend analysis to understand customer behavior patterns over time. Leveraged Apache Spark on Azure Databricks for processing big data efficiently which enabled segmentation of high-risk clients and reduced churn by 12%.

• Leveraged Python libraries such as pandas for data manipulation, NumPy for numerical operations, and Requests for API data integration to enhance the preprocessing and enrichment of customer datasets.

• Used R programming to perform survival analysis assessing customer lifetime value and churn probability which guided the design of retention strategies tailored for different client segments.

• Ensured data accuracy and consistency through rigorous validation techniques in Excel including pivot tables and conditional formatting, maintaining data integrity for 100% reliable reporting.

• Developed interactive Tableau dashboards incorporating Level of Detail calculations and advanced filters to monitor churn trends dynamically, delivering comprehensive reports and documentation for stakeholders. Education

Master’s Degree in Computer Science 08/2023 – 05/2025 University of Central Florida, Florida, USA

Bachelor’s Degree in Electronics and Communication Engineering 08/2018 – 06/2022 Sreenidhi Institute of Science and Technology, Hyderabad, India Projects

Credit Card Fraud Detection Using Big Data Pipelines

• Designed a fraud detection system by integrating transactional data from AWS S3 into PySpark pipelines, applying anomaly detection algorithms to flag unusual activity, improving early fraud alerts by 20% through real-time insights. IT Contract Renewal Prediction Dashboard

• Built a predictive model using R survival analysis and Azure Databricks to estimate IT contract renewal probabilities; visualized insights in Power BI to help account managers proactively engage at-risk clients. Certificates

Bash Scripting and Shell Programming

Data Warehouse ETL Testing & Data Quality Management Cloud Foundations (AWS Academy at NED University)

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