Sep 2022 – Oct 2022 · 1 mo · Chennai · Hybrid

• During my one-month internship at PANTECH, I worked in the Data Science department with a focus on CRM and retail analytics. My role involved applying data science techniques to enhance customer relationship management and optimize retail operations.
• Key Responsibilities:
• Data Analysis: Analyzed CRM data to identify customer behavior patterns and trends. Utilized tools such as [Python, SQL, Excel, Machine Learning, Power BI] for data cleaning and analysis.
• Customer Segmentation: Implemented customer segmentation strategies to target specific customer groups with personalized marketing campaigns.
• Predictive Modeling: Assisted in developing predictive models to forecast customer purchasing behavior and improve inventory management.
• Reporting: Generated reports and visualizations to communicate insights and recommendations to the marketing and sales teams.
• CRM Optimization: Contributed to the optimization of CRM systems by integrating data-driven insights to enhance customer engagement and retention strategies.
• Market Research: Conducted market research to understand industry trends and competitive landscape, providing actionable insights for retail strategies.

Feb 2022 – Jun 2025 · 3 yrs 4 mos · Bhubaneswar, Odisha, India · On-site

• Developed, and optimized ETL pipelines using SQL, Python, PySpark, and Databricks to process large-scale data on the Azure cloud platform.
• Implemented data ingestion processes from various sources into Azure Data Lake and Azure SQL Data Warehouse.
• Gained expertise in Azure data services, including Azure Data Lake, Azure Data Factory and Azure Databricks.
• Experienced in data modeling, and data integration techniques, including managing repositories, building pipelines, releases, and applying CI/CD processes using Git.
• Good knowledge of big data technologies such as Hadoop and Apache Spark.
• Ensured data quality and integrity through robust testing, validation, and monitoring procedures.
• Implemented data governance and security best practices in Azure to ensure compliance and secure data operations.
• Monitored and troubleshot data pipelines to guarantee timely and reliable data delivery.
• . Documented data engineering processes, workflows, and best practices for Azure and Databricks solutions.