Ashwin Srinath Sureshkumar Email & Phone Number

Toronto, On

• Leading the architecture and establishment of an Enterprise Data Lake to accommodate diverse use cases, including Analytics, processing, storage, and Reporting of large-scale, dynamically evolving data.• Ensuring the integrity and quality of reference data within the source by executing operations such as cleaning, transformation, and maintenance in a relational environment, collaborating closely with stakeholders and solution architects.• Devising and implementing a Security Framework to grant fine-grained access to objects within AWS S3 utilizing AWS Lambda and DynamoDB.• Designing and managing AWS CDK stacks for deploying serverless applications, ensuring optimal resource allocation.• Leveraging AWS Glue for ETL processes, supporting analytics, processing, storage, and reporting of large-scale, dynamically evolving data.• Utilizing Athena queries for ad-hoc analysis on stored data, facilitating streamlined reporting.• Configuring and operating Kerberos authentication principals to establish secure network communication on the cluster, conducting testing on HDFS, Hive, Pig, and MapReduce for access by new users.• Conducting comprehensive Architecture & implementation assessment of various AWS services such as Amazon EMR, Redshift, and S3.• Regularly conducting performance analysis using CloudWatch metrics to optimize resource utilization and identify areas for enhancement.• Implementing machine learning algorithms using Python to forecast user order quantities for specific items, leveraging Kinesis Firehose and S3 data lake.• Utilizing Spark SQL for Scala & Python interface, automating the conversion of RDD case classes to schema RDD.• Executing data migration from AWS S3 bucket to Snowflake by developing custom read/write snowflake utility functions using Scala.• Importing data from diverse sources such as HDFS/HBase into Spark RDD and conducting computations using PySpark to generate output responses.

Bengaluru, Karnataka, India

• Managed, configured, and scheduled resources across the cluster utilizing Azure Kubernetes Service.• Worked extensively on Azure Data Factory, encompassing data transformations, Integration Runtimes, Azure Key Vaults, Triggers, and migrated data factory pipelines to higher environments using ARM Templates.• Monitored Spark cluster using Log Analytics and Ambari Web UI, transitioned log storage from Cassandra to Azure SQL Data Warehouse for improved query performance.• Utilized Azure Data Factory to integrate on-premises data sources (such as MySQL and Cassandra) and cloud data sources (Blob storage and Azure SQL DB), applied transformations, and loaded data into Azure Synapse.• Created dashboards and visualizations for data analysis, with a focus on Microsoft products like SQL Server Reporting Services (SSRS) and Power BI.• Demonstrated proficiency in utilizing R for data analysis and statistical modeling in cloud-based environments, enriching the data engineering toolkit and facilitating advanced analytics capabilities.• Performed Data Cleaning, feature scaling, and feature engineering using pandas and numpy packages in Python.• Migrated large datasets to Databricks (Spark), administered clusters, configured data pipelines, and loaded data from ADLS Gen2 to Databricks using ADF pipelines.• Developed various pipelines to load data from Azure Data Lake into Staging SQLDB and subsequently to Azure SQL DB.

Bengaluru, Karnataka, India

• Spearheaded requirements gathering and translated them into detailed technical specifications.• Implemented seamless and efficient data flow by ingesting data into HDFS using SpringXD.• Developed MapReduce jobs utilizing Mapper, Reducer, Record Reader, and Input Formatter APIs.• Leveraged HDFS extensively for storing and managing large-scale datasets.• Collaborated with Hive to create External and Internal tables, conduct data analysis, and validate data using HiveQL.• Streamlined data loading from HDFS by formulating Hive Load Queries.• Utilized Sqoop to export data from Hive to Netezza and import data from Netezza to Hive.• Employed Informatica for efficiently loading data into final tables using bulk load methods.• Managed deployment using Git, GitHub, and Amazon EC2, deploying applications via Heroku. Extracted and analyzed data using Python libraries such as NumPy and SciPy.• Orchestrated end-to-end data processing workflows using Oozie, encompassing MapReduce, Hive, and Sqoop jobs.• Conducted Proof of Concept (POC) on AWS for transferring data from local file systems to S3.• Established and administered EMR clusters, developing Glue jobs for data processing.