Data Analyst Engineer

SADWIKA POONDLA

Data Analyst Data Engineer Business Intelligence Specialist

+1-678-***-**** ****************@*****.*** LinkedIn GitHub PROFESSIONAL SUMMARY

Results-driven Data Analyst with 4+ years of proven expertise in transforming complex datasets into actionable business insights that drive revenue growth and operational efficiency across banking, technology consulting, and e-commerce sectors. Demonstrated ability to increase fraud detection accuracy by 25% and reduce customer churn by 15% through advanced analytics and machine learning implementations. Specialized in building scalable data pipelines, predictive models, and executive dashboards that enable data-driven decision-making for C-level stakeholders. TECHNICAL SKILLS

• Programming & Analytics: Python (Pandas, NumPy, Scikit-learn), R (ggplot2, dplyr, tidyr), SQL (Advanced), JavaScript, VBA, Bash

• Database Technologies: SQL Server, MySQL, PostgreSQL, Oracle, MongoDB, Amazon Redshift, Snowflake, Google BigQuery

• Business Intelligence: Tableau, Power BI, Excel (Advanced Pivot Tables, VBA), Google Data Studio, Looker, QlikView

• Cloud Platforms: AWS (S3, Athena, QuickSight, Redshift, Glue, Lambda), Azure (Synapse Analytics, Data Factory), GCP (BigQuery, Data Studio)

• Statistical Analysis: Regression Analysis, Hypothesis Testing, A/B Testing, Time Series Analysis, Cohort Analysis, Customer Segmentation, Predictive Modeling

• Data Engineering: Apache Spark, ETL Pipeline Development, Data Cleaning & Transformation, Apache Airflow

• Machine Learning: TensorFlow, Classification Models, Clustering Analysis, Feature Engineering, Model Evaluation

• Project Management: Agile Methodologies, Jira, Confluence, Git, Cross-functional Collaboration, Stakeholder Management

PROFESSIONAL EXPERIENCE

PNC FINANCIAL SERVICES Data Engineer Pittsburgh, PA June 2024 – Present

• Engineered robust ETL pipelines processing 10M+ daily financial transactions using Python and SQL, achieving 25% improvement in fraud detection accuracy and preventing $2M in potential losses.

• Devised and implemented end-to-end data ingestion pipelines connecting 16+ disparate sources, slashing manual processing time by 40% and improving overall data quality scores by 95%.

• Architected scalable data models in AWS Redshift and Snowflake supporting real-time risk assessment for $50B+ loan portfolio, enabling compliance with regulatory requirements.

• Collaborated with cross-functional teams of 20+ analysts and data scientists to define KPI frameworks, resulting in 30% faster customer acquisition insights and 20% improved retention strategies.

• Orchestrated the use of automated data validation pipelines, increasing data pipeline efficiency by 40% and reducing data latency for critical reports by 1.5 hours on a regular basis.

• Delivered executive-level presentations to C-suite stakeholders on data infrastructure improvements, securing

$500K budget approval for platform modernization initiatives.

• Optimized SQL query performance and reporting automation, reducing data processing cycles from 6 hours to 90 minutes daily.

Environment: Python, SQL Server, AWS Redshift, Snowflake, Apache Airflow, AWS Lambda, Tableau, Power BI DXC TECHNOLOGIES Data Analyst India

June 2021 – July 2023

• Discovered operational inefficiencies within petabyte-scale datasets for 12 Fortune 500 clients using advanced SQL and Python, yielding over $5M+ in cost savings for clients.

• Developed predictive customer churn models achieving 85% accuracy, enabling targeted retention campaigns that reduced client churn rates by 15% across 500K+ customer base.

• Created 25+ interactive Power BI and Tableau dashboards integrating SAP, Oracle, and legacy systems, providing unified executive reporting for decision-making across 8 business units.

• Executed A/B testing analysis for 50+ digital transformation initiatives, optimizing conversion rates by 22% and improving user engagement metrics by 35%.

• Revolutionized data analysis by designing 12+ Python scripts that automatically extract, transform, and load data, resulting in 60% less time analysts spent on recurring manual tasks.

• Performed advanced cohort analysis and statistical customer segmentation for 2M+ users, informing marketing strategies that increased campaign ROI by 28%.

• Mentored 8 junior analysts on data analysis best practices, visualization techniques, and requirements gathering, improving team productivity by 25%.

Environment: SQL Server, Oracle, Power BI, Tableau, Python, R, Azure Synapse Analytics, Statistical Modeling FLIPKART Data Analyst India

May 2020 – May 2021

• Optimized product recommendation algorithms by analyzing customer behavior patterns across 100M+ users using SQL and Python, generating 12% increase in cross-sell revenue ($15M+ impact).

• Designed real-time inventory management dashboards in Tableau tracking 500K+ SKUs, improving demand forecasting accuracy by 20% and reducing stockout incidents by 30%.

• Conducted comprehensive market basket analysis and customer segmentation across 50M+ transactions, identifying high-value segments that contributed to 18% pricing strategy optimization.

• Spearheaded weekly competitive pricing intelligence by curating insights across 10K+ competitor products; informed data-driven pricing strategies; grew Flipkart's market share by 8% within six months.

• Performed time series forecasting on 3+ years of sales data predicting seasonal demand patterns, improving inventory planning accuracy by 20% and reducing holding costs by $2M.

• Delivered executive KPI dashboards connecting 12 data sources, enabling leadership visibility into business performance across 25 key metrics.

• Contributed to agile analytics projects while establishing data documentation standards, improving knowledge sharing efficiency by 40% across 15-member analytics team. Environment: MySQL, Tableau, Python, R, Excel, Google Analytics, E-commerce Analytics EDUCATION

• Master of Science in Computer Science Kennesaw State University, Marietta, GA, USA

• Bachelor of Technology in Computer Science and Engineering Visvodaya Engineering College, India KEY ACHIEVEMENTS

• Revenue Impact: Orchestrated data-driven optimization initiatives and predictive modeling, resulting in $22M+ in measurable business value for Flipkart through enhanced customer engagement and targeted marketing campaigns.

• Cost Reduction: Achieved $7M+ in operational cost savings through process automation and efficiency improvements

• Accuracy Improvements: Enhanced prediction models achieving 85%+ accuracy across fraud detection and customer analytics

• Process Optimization: Reduced manual processing time by 50% average across all roles through automation initiatives

• Team Leadership: Successfully mentored 8+ junior analysts and collaborated with 40+ cross-functional stakeholders

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