PowerBI developer

Snehith Gandla

Austin, TX - *****

+1-682-***-**** **************@*****.*** https://www.linkedin.com/in/snehith-g-da/ Professional Summary

Results-driven Data Analyst with 4+ years of experience turning complex data into actionable insights through Python, SQL, Power BI, and Tableau. Proven expertise in building predictive models, real-time dashboards, and automated reporting systems that improve decision-making and reduce manual effort. Led large-scale analytics initiatives at PNC Financials, including processing 45TB+ of transaction data for fraud detection, churn analysis, and executive reporting. I am skilled in cloud platforms like AWS and Azure, with certifications as a Microsoft Azure Data Engineer Associate and Power BI Data Analyst. Recognized for strong collaboration, data storytelling, and consistently delivering measurable business value in Agile environments. Skills

• Programming & Analytics: Python (NumPy, Pandas, Matplotlib, SciPy), R, SQL, Java, JavaScript

• Visualization Tools: Power BI, Tableau, Advanced MS Excel, Seaborn, Plotly, Google Data Studio, Streamlit

• Database Technologies: SQL Server, MySQL, Snowflake, Redshift, BigQuery, MongoDB, Cassandra

• Cloud & Platforms: Azure, AWS, GCP, Azure Synapse Analytics, Lambda Functions

• Machine Learning: Predictive Modeling, Customer Segmentation, Fraud Detection, A/B Testing

• DevOps & Data Engineering tools: ETL/ELT Pipelines, CI/CD, Terraform, Jenkins, Git, PowerShell Scripting, Apache Airflow

• Statistical Tools and Project Management Tools: SPSS, SAS, Jira, Agile/Scrum

• Soft Skills: Data Storytelling, Problem Solving, Communication Work History

PNC Financials Jan 2024 - Present

Data Analyst Dallas, TX

• Architected & deployed an enterprise-grade financial analytics platform using Python, SQL, and AWS services, processing 45TB+ of transaction data daily while achieving 93% accuracy in risk assessment metrics, enabling real-time decision making that reduced credit risk exposure by $4.8M annually.

• Designed and implemented an automated reporting suite using Power BI, DAX, and Python, reducing monthly executive reporting time from 2 weeks to just 1.5 days (89% reduction) while improving data accuracy by 97% through implementation of 35+ advanced validation rules and anomaly detection algorithms.

• Developed sophisticated predictive models for customer churn analysis using XGBoost and random forest algorithms, identifying at- risk accounts with 95% accuracy and enabling proactive retention strategies that saved $2.3M annually while reducing customer attrition by 28%.

• Engineered real-time dashboard system monitoring 75+ critical KPIs across 8 business functions, enabling immediate identification of market anomalies and reducing response time to adverse market changes by 84%, directly preventing $1.2M in potential trading losses during market volatility events.

• Led development of state-of-the-art machine learning models for fraud detection incorporating neural networks and ensemble methods, improving fraud identification accuracy by 86% while reducing false positives by 73%, resulting in recovery of $3.5M in potentially fraudulent transactions in first quarter.

• Established comprehensive automated data quality framework using Python, Great Expectations, and custom validation pipelines, reducing data inconsistencies by 96% across 12 enterprise systems and automating 97% of validation checks, saving 230+ hours monthly in manual verification processes.

• Conducted advanced statistical analysis on multi-dimensional customer behavior data using clustering and factor analysis, identifying previously unknown behavioral patterns that led to 58% improvement in cross-selling success rates and 32% increase in product adoption across digital banking platforms.

• Designed and implemented rigorous A/B testing framework for digital banking initiatives incorporating Bayesian methods and multi-armed bandit algorithms, increasing user engagement by 67% and digital transaction volume by 43% through data- driven feature optimizations and personalized user journeys.

• Introduced and integrated JIRA for agile project tracking and Git for version control and collaborative development in the analytics pipeline.

Infosys Jul 2021 - Jul 2022

Data Analyst Hyderabad

• Reduced patient readmission rates by 23% as measured by 30-day readmission tracking by developing predictive models using Python and machine learning algorithms to identify high-risk patients and implement targeted intervention strategies across 15 healthcare facilities

• Increased operational efficiency by 35% as measured by process optimization metrics by automating manual data extraction and reporting processes using SQL, Python, and Tableau, resulting in 40+ hours of weekly time savings for clinical staff and $1.2M annual cost reduction.

• Integrated data from EHR systems (Epic, Cerner) into analytics pipelines to support real-time clinical decision-making, while ensuring HIPAA compliance through encryption, audit logging, and role-based access controls.

• Improved patient satisfaction scores by 28% as measured by HCAHPS surveys by analyzing patient feedback data and clinical outcomes using advanced statistical techniques in R and SAS, leading to implementation of data-driven care improvement initiatives

• Enhanced decision-making speed by 45% as measured by executive reporting turnaround time by designing and implementing interactive Tableau dashboards for C-suite executives, providing real-time insights into key healthcare performance indicators, patient flow, and resource utilization

• Reduced data processing time by 60% as measured by ETL pipeline efficiency by developing automated data extraction and transformation workflows using SQL, Python, and cloud-based ETL tools, processing 2.5M+ patient records daily with 99.8% accuracy

• Awarded a Certificate of Appreciation for excellence in analytics innovation. C-Edge Technologies Nov 2019 - Jul 2021

Data Analyst Hyderabad

• Developed and deployed automated reporting solutions using Excel VBA, Power Query, and Power BI, reducing manual reporting effort by 78%.

• Designed and implemented interactive data visualization dashboards tracking 45+ key business metrics using DAX and advanced Power BI features, increasing stakeholder engagement by 85%.

• Performed comprehensive statistical analysis on multi-regional sales data using regression modeling and time series decomposition, identifying seasonal trends that led to 42% improvement.

• Implemented robust data quality assurance framework with automated validation processes, reducing reporting errors by 82% through systematic verification routines.

• Developed optimized SQL queries and stored procedures for routine data analysis across 12 database systems, improving average query performance by 67%.

• Created comprehensive documentation for analytical processes and dashboards including 200+ pages of process flows, data dictionaries, and user guides, reducing training time by 65%.

• Contributed to database optimization projects by implementing efficient indexing strategies and query rewrites, improving overall system query response time by 58%.

• Built end-to-end automated data collection scripts using Python, BeautifulSoup, and Selenium, reducing manual data gathering time by 85%.

Certifications

• Power BI Data Analyst Associate

• Azure Data Engineer Associate

Education

The University of Texas at Arlington May 2024

Master of Science, Computer Science

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