ABHILASH PAGADALA

• Delivered data-driven insights to support business operations and strategic decision-making by analyzing trends, identifying inefficiencies, and presenting key findings through visual reports and stakeholder presentations.
• Designed and deployed automated dashboards using Power BI and Tableau, integrating with SQL Server and Azure data sources to enable real-time tracking of KPIs and reduce manual reporting efforts by 50%.
• Built robust ETL pipelines using SQL and Python to extract data from ERP systems and Excel-based logs, transform it using Pandas, and load into Azure SQL Database for analysis and visualization.
• Implemented data quality checks and validation layers to identify anomalies and clean legacy data, improving report accuracy and data trust by 30%.
• Collaborated with cross-functional teams to redesign workflow processes based on data trends, contributing to a 10% reduction in project cycle time and increased resource utilization efficiency.
• Identified recurring inefficiencies in process hand-offs and resource scheduling, enabling 5% cost savings through targeted automation and resource allocation improvements.
• Presented analytical summaries to senior leadership every quarter, delivering insights that directly influenced a 15% improvement in productivity across departments.

Tools Employed: Power BI, Tableau, SQL Server, Azure SQL Database, Azure Data Factory, Python (Pandas, NumPy), Excel, PowerPoint, ERP Systems, Microsoft Teams, JIRA, Visio

• Developed automated ETL pipelines using Python, SQL, and REST APIs to pull data from flat files, databases, and cloud services, transforming and loading it into Azure SQL for business intelligence consumption.
• Performed exploratory and statistical analysis to detect performance bottlenecks, churn patterns, and efficiency gaps, leading to a 20% improvement in operational workflows across multiple client projects.
• Engineered clean, interactive dashboards in Power BI and Tableau to visualize business KPIs, sales conversion trends, and operational benchmarks, accelerating executive decision-making.
• Partnered with analytics and ML teams to prepare structured datasets and features for modeling efforts (e.g., forecasting, segmentation), resulting in a 7% improvement in model precision.
• Integrated anomaly detection methods into data pipelines to flag inconsistencies early, decreasing reporting errors by 35%.

Tools Employed: Python (Pandas, NumPy), SQL, REST APIs, Azure SQL Database, Power BI, Tableau, Excel, Jupyter Notebook, Git, Azure Data Factory, statistical libraries (SciPy), anomaly detection techniques.

• Assisted in building and automating ETL workflows with Python and SQL, extracting structured/unstructured data from APIs, Excel files, and relational databases, and loading into Azure SQL for visualization.
• Supported exploratory data analysis efforts, identifying key performance trends and inconsistencies that shaped reporting improvements and strategy updates.
• Created self-serve dashboards in Power BI to track sales performance, customer activity, and process KPIs—reducing turnaround time for data requests by 40%.
• Contributed to data preparation for predictive models by handling feature engineering tasks like date-based lags, rolling averages, and normalization techniques.
• Wrote data validation scripts and contributed to documentation standards that improved data governance and reproducibility across analytics workflows.
• Actively participated in Agile sprints and showcased findings during sprint reviews, helping to translate data insights into clear business action.

Tools Employed: Python (Pandas, NumPy), SQL, Azure SQL Database, Power BI, REST APIs, Excel, Jupyter Notebook, Git, Agile (Scrum), Confluence, and JIRA.