Sumedh Dahiwale

Project : Protective Equipment Analysis(PEA): This initiative focused on developing an automation tool for power flow and short circuit analysis, delivering detailed SCC and PQVI power flow results for each asset.

The tool provided circuit planners and designers with immediate access to load flow and fault current data based on forecast information, helping minimize power loss and identify problem areas.

It assisted electrical engineers in determining the optimal placement of protective equipment on the grid. The project included running periodic power flow analyses and generating reports detailing the load and current per phase in each feeder.

• Participated in architectural discussions to design and develop cloud-based applications on AWS.
• Integrated per-phase load allocation in power flows for more accurate load distribution between phases, improving overall power flow results using AWS services.
• Developed AWS Glue jobs with PySpark to transform data from Glue catalog tables and write the power flow results to DynamoDB , which are then consumed by the iGrid visualization tool.
• Developed APIs using API Gateway to securely transmit PEA load flow and SCC data across different teams.
• Developed and implemented Docker -based testing environment to locally test AWS glue and lambda jobs, significantly reducing testing time and improving deployment efficiency.
• Proficient in managing sensitive data using vault technologies and adept at leveraging parameter store to secure data in cloud environments.
• Identified key areas for process improvement and implemented strategic strengthening activities to enhance product reliability and scalability.
• Conducted in-depth analysis of S3 data leveraging Athena , delivering timely and actionable business insights as required.
• Implemented cost-effective columnar file storage on Amazon S3, optimizing data storage strategies and leveraging partitioning techniques to manage costs associated with data retrieval.
• Developed unit test cases utilizing unittest and Pytest frameworks, effectively mocking AWS services with the moto library. These unit tests were instrumental in ensuring the reliability and accuracy of our codebase.
• Developed and maintained CI/CD pipelines using Concourse and Terraform Enterprise increasing deployment speed and reliability.

• Worked on creating Kafka topics to ingest data from different sources in Delta tables using Databricks.
• Worked on creating various frameworks such as Hive Auto Partition, File Optimizer, Alerting framework & Logging framework by making use of AWS services
• Gained proficiency in writing Python code for serverless applications.
• Automated the process of deployment by using Terraform.
• Worked on various AWS services such as Lambda, Glue, Kinesis, CW, S3, SNS, SQS, EMR, Athena, Redshift ++
• Worked on migrating Hive ( MySQL) metastore to AWS Glue metastore
• Worked on protection strategies using KMS, implementation of key policies for customer managed CMK, AWS managed keys
• Worked on migrating source data across various AWS accounts.

• Developed Hive Queries to analyze the data in HDFS to identify issues and behavioral patterns
• Gained knowledge in process improvement, normalization/de-normalization, data extraction, data cleansing, data manipulation
• Gained experience with handling different file format such as XML, JSON, AVRO, ORC and PARQUET format in Hive using different SerDes.
• Implemented Dynamic partitions and bucketing in Hive for efficient data processing
• Responsible for generating actionable insights from complex data.