Pranav M

• Manufactured Aligners For patients, we collected patient data from S3 and created
an AWS GLUE job for transforming data through the Batch Pipeline.
• We used AWS Crawler to extract patient data from the S3 bucket performed the
transformations using AWS Glue, and also converted files CSV and JSON into
relational datasets.
• Performed Data Cleaning, Data processing, and Data Mapping transformations
using the Pyspark Scripts.
• Created serverless architecture using Lambda in Glue and Kinesis.
• Stored transformed data in Snowflake and built visualizations and reports in Tableau
for a dynamic dashboard using Snowflake data.
• Experience working with AWS services like EC2, RDS, and Lambda to build reliable
and scalable Applications.
• Proficiently migrated the project, moving a legacy on-premises data warehouse to
Snowflake, resulting in a 50% reduction in query response times and operational
costs.
• Familiarity with AWS database migration services, including schema and data
migration from on-premises databases to RDS instances.
• Designed and implemented data warehousing solutions using Amazon Redshift,
optimizing schema design and query performance for large datasets.
• Used Apache Airflow Dags to schedule the pipelines from AWS S3 to Snowflake.
• Implement CI/CD pipeline for Code Deployment using Jenkins.
• Proficient in administering Amazon RDS instances (MySQL, PostgreSQL, SQL Server) for
optimal performance, security, and availability.
• Designed and implemented a high-volume pipeline data processing using Oracle 19
database and Oracle data integration.
• Utilized Agile methodology for iterative application development, weekly sprints, and
customer reporting backlogs.

Manufactured Aligners For patients, collected patient data from S3 and created an AWS GLUE job for transforming data through the Batch Pipeline.
Used AWS Crawler to extract patient data from the S3 bucket and performed the transformations using AWS Glue.
Converted files CSV and JSON into relational datasets.
Performed Data Cleaning, Data processing, and Data Mapping transformations using the Pyspark Scripts.
Created serverless architecture using Lambda in Glue and Kinesis.
Stored transformed data in Snowflake and built visualizations and reports in Tableau for a dynamic dashboard using Snowflake data.
Worked with AWS services like EC2, RDS, and Lambda to build reliable and scalable Applications.
Migrated the project, moving a legacy on-premises data warehouse to Snowflake, resulting in a 50% reduction in query response times and operational costs.
Familiarity with AWS database migration services, including schema and data migration from on-premises databases to RDS instances.
Designed and implemented data warehousing solutions using Amazon Redshift, optimizing schema design and query performance for large datasets.
Used Apache Airflow Dags to schedule the pipelines from AWS S3 to Snowflake.
Implemented CI/CD pipeline for Code Deployment using Jenkins.
Administered Amazon RDS instances (MySQL, PostgreSQL, SQL Server) for optimal performance, security, and availability.
Designed and implemented a high-volume pipeline data processing using Oracle 19 database and Oracle data integration.
Utilized Agile methodology for iterative application development, weekly sprints, and customer reporting backlogs.

Designed and implemented an ETL pipeline using Azure Databricks to process and store large amounts of property insurance data from Azure Data Lake Storage.
Developed Apache Spark ETL application using PySpark and extracted property insurance data from different sources like Vertica and SQL Server.
Used PySpark to clean, transform, and aggregate data with proper file and compression types as per requirement before writing data to Azure data lake storage.
Migrated property insurance data from Azure Data Lake storage to Azure SQL Database using Azure Data Factory and Azure DataBricks notebooks.
Used PowerBI and generated dynamic visualizations for business purposes.
Created and maintained optimal data pipeline architecture in Azure using Data Factory and Azure Data Factory.
Troubleshot and resolved issues with a Splunk environment.
Developed CI/CD pipelines to filter the data based on the application and deploy the application to a higher environment.
Scheduled pipelines using Cron Syntax in Airflow and created the workflow using Azure Logic Apps.
Worked with Azure Kubernetes Service (AKS) for integrating CI/CD pipelines.
Created and configured Kubernetes clusters on AKS using Azure Portal and Azure CLI.
Wrote queries and manipulated data in NoSQL databases using the appropriate APIs and tools.
Implemented Map Reduce jobs in Python for data cleaning and data processing.
Worked in SCRUM Methodology for designing, analyzing, and developing the pipelines, and testing the use cases for the business.

Installed, configured, and maintained Apache Hadoop clusters for application development based on the requirements.
Extracted files from Hadoop and dropped them on an hourly basis into S3.
Wrote Pig Scripts to generate MapReduce jobs and performed ELT procedures on the data in HDFS.
Worked with Elasticsearch Query DSL and related search technologies, such as Lucene and Kibana.
Used Sqoop to channel the data from different sources of HDFS and RDBMS.
Developed real-time data ingestion application using Flume and Kafka.
Wrote code and created Hive jobs to parse logs and structure them in tabular format for effective querying.
Understood how NoSQL databases fit into the larger context of modern software architectures, including microservices and cloud computing.
Integrated NoSQL databases with other technologies such as Apache Kafka, Apache Spark, and Elasticsearch.
Designed and implemented database solutions using PostgreSQL, including database schema design, query optimization, and performance tuning for customers' data.
Wrote SQL queries and created models using DBT's templating language.
Used Python for loading data from different sources to a data warehouse to perform data aggregations for business intelligence.
Developed spark applications using PySpark and Spark-SQL for data extraction, transformation, and aggregation for multiple file formats.
Used Quick Sight for analysis of applications for business purposes.

Developed custom reports using HTML, Python, and MySQL and also developed monitoring tools using Python.
Used NumPy, SciPy, and Matplotlib libraries for n-dimensional representation of data and plotting graphs.
Worked with PostgreSQL for developing SQL reports that meet client expectations for the application.
Worked on Python Open stack APIs and developed tools using Python, Shell scripting, and XML to automate some of the menial tasks.
Designed and developed the UI of the website using HTML5, XHTML, AJAX, CSS3, and JavaScript.
Worked with SQL, NoSQL, MongoDB, DynamoDB, and PostgreSQL.
Worked with JSON-based REST Web services and Amazon Web Services (AWS).
Involved in Sprint planning sessions and participated in the daily Agile SCRUM meetings and monitored JIRA (Agile).