Nikhil Saindane

• The Data Engineering team consumes data from our client, where they want to extract valuable insights from customer data, including the calculation of credit scores and identification of loan availing patterns. This strategic approach aids in overcoming obstacles, fostering business growth, and cultivating a more reliable customer base.
• Daily, the substantial volume of generated data undergoes meticulous transformation to a format readily interpretable by analysts, ensuring seamless consumption of information.
• Implemented advanced Data Engineering concepts for data ingestion, transformation, and processing, handling approximately 20GB of data efficiently using technologies such as Spark, Spark SQL.
• Our workflow involves the systematic handling of raw data, starting from ingestion into the raw layer, followed by meticulous cleaning and organization into the clean layer. After processing, the refined data is stored in the processed layer, facilitating streamlined access for our analyst team.
• Our contribution has empowered the customers to make well-informed decisions based on insights derived from processed data, thereby enhancing their existing business strategies.

Acquired comprehensive knowledge of big data technologies during a six-month internship.
Demonstrated proficiency in designing and optimizing data pipelines.
Engaged in data integration tasks to enhance operational efficiency.
Successfully managed data storage solutions to ensure data integrity and accessibility.
Developed real-world experience in the application of big data technologies.
Enhanced understanding of big data technologies, positioning myself as a valuable asset in the field.

The Data Engineering team consumes data from our client, where they want to extract valuable insights from customer data, including the calculation of credit scores and identification of loan availing patterns.
This strategic approach aids in overcoming obstacles, fostering business growth, and cultivating a more reliable customer base.
Daily, the substantial volume of generated data undergoes meticulous transformation to a format readily interpretable by analysts, ensuring seamless consumption of information.
Implemented advanced Data Engineering concepts for data ingestion, transformation, and processing, handling approximately 20GB of data efficiently using technologies such as Spark, Spark SQL.
Our workflow involves the systematic handling of raw data, starting from ingestion into the raw layer, followed by meticulous cleaning and organization into the clean layer.
After processing, the refined data is stored in the processed layer, facilitating streamlined access for our analyst team.
Our contribution has empowered the customers to make well-informed decisions based on insights derived from processed data, thereby enhancing their existing business strategies.

Working on Electronic Medical Record System.
Writing Structure Query Language (SQL) for data verification to perform Backend Testing against MS SQL/ My SQL Database.
Identifying and raising the defects.
Performed different levels of testing (functional, regression) on interface and resolved them as per the client’s requirements.
Developed and maintained overall test methodology and strategy.
Documenting Test plans, Test cases and editing, executing Tests cases and Test scripts Using Manual.
Analysed User Requirements and Technical.
Used Quality Centre to store all testing results, metrics, created Test Cases, and Defect tracked and reported the bugs using JIRA.
Interact with Developers and Business Analysts to perform various types of testing throughout.
Software Testing Life Cycle (STLC).
Tested proprietary web-Based application for medical imaging software.
Performing RCA (Root Cause Analysis) on Reported issues and to provide permanent solutions.
Perform peer review of Test Cases
SQLYOG: Working on Data Manipulation & Validation of product.
Apache Tomcat: Provide the supports to the front-end of the application.
WinScp: To check the transmission of data files on client server.