Data Analyst Business Intelligence

Uday Shanker Gulbi Data Analyst

***************@*****.*** 309-***-**** USA LinkedIn

Summary

Detail-oriented Data Analyst with 3+ years of experience in data analysis, statistical modeling, and business intelligence. Proficient in SQL, Python, R, Power BI, and Tableau. Skilled in ETL pipelines, predictive analytics, and delivering insights for data-driven decisions. Experienced with large datasets, data quality, and Agile teams. Passionate about turning complex data into impactful business solutions. Technical Skills

● Programming & Scripting: Python (pandas, NumPy, Seaborn, Statsmodels), R (tm, LDA), PySpark, DAX, Bash/Shell Scripting

● Data Visualization: Power BI (Power Query, AI visuals, custom filters), Tableau (LOD, KPI indicators, drill-down filters), Seaborn, Matplotlib, Looker

● Database & Query Languages: SQL, Amazon Redshift, PostgreSQL, MySQL, Snowflake, Google BigQuery

● ETL & Data Integration: AWS Glue, Azure Data Factory, Azure Data Lake, Apache Airflow, dbt (Data Build Tool)

● Statistical & Predictive Analytics: Logistic Regression, Predictive Modeling, Clustering, Time-Series Analysis, Text Mining, Topic Modeling (LDA), A/B Testing, Bayesian Analysis

● Business Intelligence Tools: Power BI, Tableau, Google Data Studio, Qlik Sense

● Cloud Platforms: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP)

● Data Validation & Quality: ETL Validation Checks, Data Cleaning, Integration Testing, Data Governance, Data Profiling, Anomaly Detection

● Agile & Collaboration: Agile Environments, Cross-Functional Team Collaboration, Requirement Sessions, JIRA, Confluence, Version Control (Git/GitHub)

● Other Tools & Technologies: Excel (Power Pivot, Power Query), Alteryx, SAS, Jupyter Notebooks, VS Code Professional Experience

Data Analyst, PNC Financial Services 10/2024 – Present Remote, USA

● Worked on Credit Risk Assessment Analysis, collaborating with cross-functional teams in an Agile environment to assess customer creditworthiness, finding potential risks, and improving risk sorting accuracy by 18%, enabling more reliable credit decision-making.

● Utilized SQL with Amazon Redshift for high-performance data retrieval and PostgreSQL for efficient querying of large credit risk datasets, improving data processing speed by 25% and enhancing overall data integrity and quality.

● Performed logistic regression and predictive analytics to identify factors affecting loan defaults, achieving a 92% accuracy rate in forecasting credit risk, leading to more precise loan approval processes and reducing defaults by 10%.

● Employed Python libraries such as pandas for data manipulation, NumPy for numerical computations, Statsmodels for statistical analysis, and Seaborn for advanced data visualization, enabling comprehensive analysis of credit risk factors and their impact.

● Ensured data integrity by performing data validation during ETL processes, incorporating AWS Glue for seamless data transformation and integration, and DAX to calculate key credit risk metrics, reducing reporting errors by 20%.

● Developed advanced Power BI dashboards, integrating Power Query for dynamic data transformation, AI visuals for predictive insights, and custom filters for drill-down analysis, boosting user engagement by 30% in credit risk monitoring and reporting. Data Analyst, Western Digital 01/2021 – 08/2023 Bangalore, India

● Collected and aggregated customer sentiment data from product reviews, support tickets, and social media. Collaborated with CX and product teams in agile environments and led requirement sessions to guide marketing and product development plans effectively.

● Designed and optimized SQL queries to manage customer data. Built ETL pipelines using Azure Data Factory and processed over millions records from CRM, social channels, and third-party review platforms to ensure clean integration.

● Conducted sentiment trend analysis using clustering and time-series methods. Identified three recurring product issues that helped reduce negative feedback by 31% and supported the product team in improving customer experience significantly.

● Leveraged Python along with NumPy and pandas to automate text preprocessing workflows. Cleaned and transformed feedback entries weekly which reduced manual data handling time by 95% and increased model reliability.

● Used R and the tm package to perform text mining and topic modeling. Applied LDA to detect latent topics in customer complaints which helped shift marketing priorities and increased campaign engagement by 22%.

● Implemented ETL workflows using Azure Data Factory and Azure Data Lake. Performed validation checks on ingested data which resulted in 99.7% accuracy across customer feedback records from multiple sources and geographic locations.

● Built dynamic Tableau dashboards with KPI indicators and drill-down filters. Utilized Level of Detail (LOD) expressions to enable segmented analysis by region and sentiment which improved product decision-making speed by 35%. Education

Bradley University Peoria, IL, USA

Master of Science in Computer Science 08/2023 — 05/2025 PES University Bangalore, Karnataka, India

Bachelor of Engineering in Electronics and Communication 08/2019— 05/2023

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