Ajith Amin

Data Engineer with 4+ years of experience in building and maintaining scalable data pipelines using PySpark, SQL, Python, and Azure services such as ADF, Databricks, and ADLS. Strong experience in ETL/ELT development, Delta Lake, and data quality management.

• 4+ years of experience working on Azure-based data engineering solutions using Azure Data Factory, Azure Databricks, and Azure Data Lake.
• Hands-on experience in building ETL/ELT pipelines involving data extraction, transformation, and loading from multiple source systems using Azure Data Factory (ADF).
• Designed and implemented end-to-end data integration workflows on Azure Cloud using ADF and Databricks.
• Developed and maintained Databricks notebooks using PySpark for large-scale data processing and transformation.
• Implemented efficient data ingestion strategies leveraging Delta Lake features such as schema evolution and optimized storage.
• Managed and secured sensitive credentials using Azure Key Vault and integrated them with data pipelines.
• Designed and implemented data pipelines following the Medallion Architecture (Bronze, Silver, Gold) to ensure scalable ingestion, improved data quality, and analytics-ready datasets.
• Integrated Azure Logic Apps with data pipelines for orchestration and monitoring, enabling automated email notifications and alerts on pipeline failures to support faster incident response.

• Helped build and maintain daily data pipelines using Azure Data Factory and Databricks, making sure data landed correctly and was easy for reporting teams to use.
• Looked after historical policy and claim data going back more than five years, which helped teams understand how policies and claims changed over time.
• Prepared clean, reliable datasets used to calculate business metrics like claims vs premium ratio, average settlement time, and claim frequency across 1M+ policies.
• Worked with Azure Synapse Serverless SQL to support reporting use cases by exposing clean, query-ready views on top of the data lake.
• Used GitHub for version control, following a simple branching process and raising pull requests for data pipeline and notebook changes.
• Supported deployments across environments (DEV, QA, PROD) by validating data, checking pipeline runs, and coordinating fixes during release windows.
• Helped investigate and fix data defects and pipeline failures, working with logs and sample data to identify root causes and prevent repeat issues.
• Monitored daily batch runs and helped troubleshoot failures to make sure reports and dashboards were delivered on time.

• Received "On-the-Spot Performer" award for quick resolution of last-minute client data streaming requirements.
• Awarded the Team Excellence Award for outstanding collaboration and successful delivery of data pipeline solutions.