Naveen Bandi
Hands-on experience with SQL, PySpark, Python, Azure Data Factory, and Databricks through projects and practical learning. Worked on building data pipelines, performing ETL/ELT processes, and preparing clean,analytics-ready datasets in cloud-based environments .Good understanding of data warehousing concepts, data quality, and basic performance optimization. Comfortable collaborating with teams, learning new tools and solving data-related problems. Actively looking to apply and grow these skills in a real-world environment
Hands-on experience with SQL, PySpark, Python, Azure Data Factory, and Databricks through projects and practical learning. Worked on building data pipelines, performing ETL/ELT processes, and preparing clean,analytics-ready datasets in cloud-based environments .Good understanding of data warehousing concepts, data quality, and basic performance optimization. Comfortable collaborating with teams, learning new tools and solving data-related problems. Actively looking to apply and grow these skills in a real-world environment
Experience
- Data Warehouse and Analytics Project
- . Worked on a data warehouse project to understand how raw data is collected, stored, and prepared for analysis
- . Implemented Medallion Architecture (bronze, silver, and gold layers) approach to organize raw, cleaned and
- final data.
- . Ingested raw ERP and CRM CSV data into SQL Server and maintained source-level data in the bronze layer.
- . Performed data cleansing, standardization, and normalization in the silver layer to improve data quality.
- . Designed Star Schema–based fact and dimension tables in the gold layer for analytical querying.
- . Learned key data warehousing concepts such as the ETL process, fact tables, and dimension tables
- Azure End-To-End Data Engineering Project
- .Used Azure Data Factory (ADF) to create data pipelines that pull data from APIs and load it into the Bronze (raw)
- layer of Azure Data Lake. Used parameters and loops to make the pipelines reusable.
- Cleaned and transformed data using Azure Databricks (PySpark) by applying date functions, string operations
- (split, concat_ws), and group by to move data from the bronze layer to the silver layer.
- Set up secure access between Databricks and Azure Data Lake by using System-Assigned Managed Identity
- and assigning the required roles.
- Used Azure Synapse Analytics (Serverless SQL Pool) to read data directly from the Data Lake using
- OPENROWSET, following the Lakehouse architecture.
- Created Gold layer external tables using CETAS in Synapse Analytics and connected the data to Power BI for
- reporting and dashboards
Skills
Education
B.Tech (Bachelor of Technology) in Mechanical Engineering
CMR Engineering College,Hyderabad