o Leveraged AWS and Snowflake for scalable and secure data engineering solution.o Utilized Python and PySpark in Databricks for data extraction, transformation, and consolidation.o Utilized a serverless AWS sevices for running the solution, ensuring scalability and cost-efficiency.o Provided production support with job monitoring and incident resolution, ensuring timely resolution of incidents and service requests.o Adhered to companies' data governance standards and implemented security measures to protect sensitive data.

o Designed and implemented robust databases using SQL and Azure to store and manage large-scale datasets. o Performed data extraction, transformation, and loading (ETL) from various sources into Azure Blob Storage. o Developed data models, including schemas and ER diagrams, to structure and organize data effectively. o Integrated with SQL Server and created views to facilitate efficient data retrieval and analysis. o Conducted comprehensive exploratory data analysis to uncover valuable insights from the data. o Developed interactive visualizations using Power BI to present data-driven insights to business stakeholders.

A DATA CURATION SYSTEM AND PREDICTIVE MODEL FOR UNCONFINED CONCRETE STRENGTH:o DCE receives huge concrete test data from various construction sites in PDF formats. Their current network-attached storage system complicates data retrieval and analysis. Also, there is a great need to predict the concrete strength and identify key influencing factors, thereby boosting operational efficiency, cost-cutting, and informed decision-making.o Performed data scraping from PDFs using python libraries like pdfplumber, regex, camelot and transformed it into a structured format. Implemented a Machine Learning model to predict concrete strength, using Artificial Neural Networks.o Conducted Exploratory data analysis and visualized the data using Tableau and Python libraries (Altair) to gain insights.

- Worked as a Data Engineer for 4 years, leading the development and support of a data warehouse for NGM FFA application, by performing Data Transformations, Orchestration, Analysis, Visualization.PROJECTS:-----------FIELD FORCE APPLICATION IMPLEMENTATION AND DARA WAREHOUSING:------------------------------------------------------------------------------------- Worked as a Data Engineer for 4 years, leading the development and support of a data warehouse for NGM FFA application, by performing Data Transformations, Orchestration, Analysis, and Visualization.Data Orchestration:---------------------* Created ETL pipelines to ingest data from various sources into the Data Warehouse hosted in Azure.* Used Azure Data Factory, PySpark and SQL in Databricks to perform data orchestration and transformations.* Built stored procedures to extract and move data through pipelines.* Employed the ActiveBatch pipeline scheduling tool and monitored them on daily basis.* Stored active records in Azure SQL Database in the data warehouse and generated reports.Data Analytics and Reporting:--------------------------------* Conducted Exploratory Data Analysis using SQL and Python to identify patterns, trends, and anomaliesto gain insights about the data. * Power BI and Python Data Visualization libraries (such as Altair, Matplotlib, Ggplot, Seaborn) are used to present the data in a way that is easy for clients to understand and interpret the large and complex data and help business make informed decisions.ACHIEVEMENTS: ------------------- Won best project award in Wipro's Energy and Natural Utilities sector for Field force application implementation. - Received multiple client and business head appreciations for Automation ideas and team leadership.