AISHWARYA RAMAN

The McAfee Mosaic Portal is being upgraded from Angular CLI version 7.3.6 to version 12. Completed upgrades to Angular 8, 9, and 10, with work on Angular 11 and 12 in progress.

• Analyzed Alteryx workflows and mapped migration to Databricks.
• Designed data architecture using the Medallion approach with Bronze, Silver, and Gold data layers.
• Developed and tested ETL pipelines in PySpark to replicate Alteryx functionality in Databricks.
• Managed historical and incremental data loads to ensure data completeness and currency.
• Optimized data storage and query performance through data partitioning and Spark tuning.
• Collaborated with cross-functional teams to align migration with business needs.
• Migrated SAS code to PySpark in AWS Glue and conducted BAT testing.

Managed ALPACA data products for HP Print Consents, handling daily data reports and storage in delta tables based on parquet format.

• Implemented data integration techniques, including ETL processes and data pipelines.
• Designed and optimized data models for both relational and NoSQL databases.
• Led the implementation of historical and incremental data loads in AWS Glue using PySpark.
• Developed and optimized data processing workflows in Azure Databricks.
• Managed data storage and processing in AWS S3 and Redshift.
• Deployed and managed big data solutions across AWS, Azure, and Google Cloud platforms.
• Created and optimized ADF pipelines for data transfer and transformation tasks.
• Ensured data governance, data lineage, and metadata management.
• Collaborated with cross-functional teams to align data solutions with business goals.
• Guided junior team members and resolved complex data-related challenges.
• Led and managed big data projects, optimizing costs and monitoring resource usage.

• Developed a data lake for Sonova to efficiently store and access structured and unstructured data from various sources.
• Participated in business meetings to define deliverables based on requirements.
• Designed and architected the data lake in Azure, utilizing Pyspark and Python.
• Created EventHub endpoints for data ingestion from six sources, handling approximately 1 TB of data.
• Managed data ingestion processes and ensured seamless data transfer between sources using Azure Data Factory (ADF).
• Integrated AWS data sources with Azure Data Lake using Azure Databricks.
• Built and deployed Docker containers to enhance developer workflows and provide updated images for predictive models.
• Established a CI/CD pipeline using TeamCity and GitHub to automate builds and deployments.
• Collaborated with the R&D team to implement data encryption for sensitive information during ingestion.
• Help R&D team with the implementation of encrypting data while pushing into the Data lake so that sensitive data doesn't exposed.

• Specifically focus on building out Cloud Services to help generate new revenue streams
• Work closely with Architects and technical product managers to translate overall system architecture and product requirement into well designed and implemented software components
• Creation of different AWS service like API, Lambda to build a Serverless application
• Picking the right AWS services for the application
• Creating different S3 buckets and write Lambda to move files from S3 to instance and FTP server to S3
• Completes database architecture and design to help build the foundation of new systems
• Develop logical data flows that support project requirements and guides colleagues in appropriate and effective use of data
• Evaluates KPIs to identify trends and evaluate the success of business initiatives and creates models and reports to demonstrate observations and clearly communicate recommendations to senior management
• Built and deploy Docker containers to improve developer workflow, increase scalability, and optimizing speed
• Used Continuous integration tools like Jenkins, for build process automation and deployment process via Git Hub.

• Designed AWS architecture for cloud-based big data solutions with EMR, S3, and Lambda.
• Developed strategies and roadmaps for modernizing data platforms.
• Created real-time and batch data ingestion architectures using Lambda, Kafka, Spark, HBase, Sqoop, and Hive.
• Implemented and tested large AWS clusters and proof-of-concept projects.
• Delivered presentations on Hadoop best practices and architecture.
• Mentored architects, reviewed architectures and code, and solved Hadoop issues.
• Wrote and integrated scripts to enhance user experience.
• Built and deployed scalable Hadoop ecosystems.
• Managed project feasibility, costs, and multi-disciplinary teams.

• Designed a real-time analytics and ingestion platform using Spark and Kafka.
• Architected Hadoop applications and recommended appropriate solutions and technologies.
• Collaborated with clients and onsite counterparts for solution design and consulting.
• Extracted log files and loaded them into HDFS using Sqoop for analysis.
• Provided consulting on Big Data use cases and designed scalable Hadoop clusters.
• Automated data ingestion into HDFS using Oozie workflows.
• Translated high-level solutions into actionable deliverables and guided team implementation.
• Led a team of 3+ on various Big Data solutions and managed infrastructure setup for POCs.
• Prepared high-level designs, assigned tasks, tracked progress, and conducted status calls.
• Presented demos to clients, received appreciation, and successfully transitioned POCs to project phase.Got Appreciation in implementing the project and succeeding in making the Poc move to Project phase.

• Understood the architecture of the Big Data ecosystem and its components.
• Processed large sets of structured, semi-structured, and unstructured data.
• Designed and implemented HQL scripts and wrote MapReduce jobs in Java and Pig.
• Imported and exported log data into HDFS using Flume, and transformed data with Pig.
• Loaded, transformed, and supported large data sets, including Hadoop configuration and support.
• Analyzed data stores to uncover insights and maintained security and data privacy.
• Consulted with clients to determine requirements for new structures.

• Understood Big Data ecosystem architecture and its components.
• Designed and implemented HQL scripts and recommended architectural improvements.
• Wrote MapReduce jobs in Java and Pig; imported and exported data to HDFS and Hive using Sqoop.
• Loaded and transformed large data sets, managed data from various sources, and supported MapReduce programs.
• Installed, configured Hive, created tables, and wrote Hive UDFs and queries.
• Pulled data from FTP servers into Hive using Oozie workflows; generated result tables and analyzed systems.
• Developed High-Level and Low-Level Designs (HLD and LLD), coded, and tested system functionalities.
• Conducted weekly status meetings and prepared monthly reports; ensured effective onshore-offshore coordination.

• Captured requirements from stakeholders and led enhancements and development projects.
• Assigned tasks to team members and addressed bug fixes.
• Implemented PowerCenter/PowerExchange for data conversion and migration from legacy IMS and VSAM mainframe systems to Oracle 11i.
• Consulted on infrastructure and technology to ensure integration and alignment with other architecture domains.Consulted with stakeholders and other project team members in the areas of infrastructure, technology, platform, middleware, database, capacity, physical deployment, and service management to ensure consistency, alignment and integration with other architecture domains.

• Interacted with the client to capture requirements and managed enhancements and development based on those needs.
• Assigned tasks to team members and handled bug fixes.
• Implemented PowerCenter/PowerExchange for data conversion and migration from legacy IMS and VSAM mainframe systems to Oracle 11i.
• Utilized mainframe technologies such as IMS, VSAM, and DB2 in data migration and integration processes.