Navya Sireesha Chamarthi

Project: Telstra Installed Migrations (TIM)

• Optimized data processing by implementing efficient ETL pipelines and streamlining database design.
• Collaborated on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability.
• Designed and developed scalable data processing applications using Scala and Spark, handling large telecom datasets.
• Built configurable data ingestion frameworks integrated with PostgreSQL, improving reusability and reducing development effort.
• Owned end-to-end pipeline lifecycle: Development, testing, and deployment.
• Integrated multiple source systems into a unified processing layer to improve flexibility and scalability.
• Project: B2B Data Platform (Telstra)
• Developed and maintained ADF pipelines to ingest data from multiple sources into curated layers in ADLS.
• Implemented complex business transformations and performed validation to ensure data accuracy.
• Collaborated with business stakeholders, RunOps, and project managers to deliver reliable data solutions.
• Prepared deployment documentation and executed production releases with minimal downtime.
• Scheduled and monitored workflows using Control-M.
• Project: MITIE
• Monitored production pipelines and resolved failures, improving system reliability.
• Implemented alerting mechanisms using Azure Logic Apps, reducing manual effort and improving SLA adherence.
• Automated recurring issue handling, reducing pipeline failure rates.
• Automated alerting using Azure Logic Apps, reducing manual effort by 40%.
• Reduced pipeline failures through proactive monitoring and automation.

• Designed and developed ETL workflows using ADF and stored procedures.
• Built pipelines to move and transform data from Data Lake to Salesforce.
• Implemented rejection handling by storing invalid records in Azure Blob Storage.
• Automated alerts using Azure Logic Apps for business notifications.
• Wrote SOQL queries and validated Salesforce data.
• Optimized SQL queries and resolved production defects.

• Developed ETL pipelines using ADF and Azure Databricks.
• Built incremental ingestion pipelines from Salesforce to ADLS.
• Processed and stored data in Parquet format for analytics.
• Implemented data quality checks and validation in Databricks.
• Performed transformations and loaded curated data into integration tables.
• Enhanced data quality by performing thorough cleaning, validation, and transformation tasks.
• Compiled, cleaned and manipulated data for proper handling.