AANANDHENE M

Designed and maintained scalable batch and real-time data pipelines using Python, SQL, Apache Kafka, Apache Flink, PostgreSQL, and Redis, processing 5M+ records per day across production workloads with zero data loss., Built and orchestrated ETL/ ELT workflows using Apache Airflow, reducing end-to-end data delivery time by 50% through automated scheduling, dependency management, and parallel task execution., Resolved 20+ critical production pipeline incidents, performing root-cause analysis, bug fixes, and performance tuning to restore SLA-compliant data delivery within tight timelines., Optimized transformation logic, query performance, and processing pipelines, improving overall pipeline efficiency by 50%+ and reducing manual intervention by over 30%., Implemented Databricks Lakehouse (Bronze/Silver/Gold) pipelines and Snowflake data warehouse solutions to support analytics, reporting, and AI platform use cases across the organisation., Enforced data quality, governance, and security best practices across all pipeline layers, ensuring analytics-ready datasets with full lineage and validation coverage., Collaborated with data scientists, analytics, and product teams to deliver accurate, timely datasets powering business reporting and data-driven decisions., Worked with CI/CD workflows, Git/ GitHub version control, and Jenkins for reliable deployment and engineering standards documentation.

Designed and maintained scalable batch and real-time data pipelines using Python, SQL, Apache Kafka, Apache Flink, PostgreSQL, and Redis, processing 5M+ records per day across production workloads with zero data loss.
Built and orchestrated ETL/ ELT workflows using Apache Airflow, reducing end-to-end data delivery time by 50% through automated scheduling, dependency management, and parallel task execution.
Resolved 20+ critical production pipeline incidents, performing root-cause analysis, bug fixes, and performance tuning to restore SLA-compliant data delivery within tight timelines.
Optimized transformation logic, query performance, and processing pipelines, improving overall pipeline efficiency by 50%+ and reducing manual intervention by over 30%.
Implemented Databricks Lakehouse (Bronze/Silver/Gold) pipelines and Snowflake data warehouse solutions to support analytics, reporting, and AI platform use cases across the organisation.
Enforced data quality, governance, and security best practices across all pipeline layers, ensuring analytics-ready datasets with full lineage and validation coverage.
Collaborated with data scientists, analytics, and product teams to deliver accurate, timely datasets powering business reporting and data-driven decisions.
Worked with CI/CD workflows, Git/ GitHub version control, and Jenkins for reliable deployment and engineering standards documentation.

Built ETL workflows and SQL transformations for data integration and migration requirements, supporting 1M+ record migrations between SQL Server, Kafka, and PostgreSQL., Supported real-time streaming implementations using Apache Kafka and Apache Flink for high-volume transactional data processing., Contributed to analytics and reporting solutions using Microsoft Power BI and Apache Superset for TaxBandits and TruckLogics products., Performed testing, debugging, validation, and pipeline monitoring in Agile sprint environments, improving delivery reliability.

Built ETL workflows and SQL transformations for data integration and migration requirements, supporting 1M+ record migrations between SQL Server, Kafka, and PostgreSQL.
Supported real-time streaming implementations using Apache Kafka and Apache Flink for high-volume transactional data processing.
Contributed to analytics and reporting solutions using Microsoft Power BI and Apache Superset for TaxBandits and TruckLogics products.
Performed testing, debugging, validation, and pipeline monitoring in Agile sprint environments, improving delivery reliability.