Saravanan Sekar

Client : Commonwealth Bank

Title : Real-Time Data Pipeline & Data Warehouse Enhancement for Investment Banking.

• Designed and implemented real-time data pipelines using Apache Kafka, AWS Kinesis, and PySpark for processing high-volume trading and transaction data.
• Built scalable ETL/ELT workflows using AWS Glue, Lambda, and S3 to ingest structured and semi-structured financial data.
• Enhanced data warehouse performance in Amazon Redshift / Snowflake through query optimization, partitioning, and indexing techniques.
• Developed star and snowflake schema models for trade analytics, risk management, and regulatory reporting.
• Reduced data processing latency by 30–40% by optimizing pipeline orchestration using Apache Airflow.
• Integrated multiple data sources including market feeds, customer transactions, and portfolio systems.
• Ensured data quality and governance using validation frameworks and data lineage tracking.

Client : Bremer Bank

Title: Cloud Based Data Warehouse Implementation for Investment Banking

• Designed and deployed end to end ETL pipelines using Python,SQL and pyspark, platforms.
• Migrated legacy on-premises systems to AWS Redshift, Snowflake, and S3 ensuring scalability, performances and high availability.
• Developed and optimized AWS Glue jobs for large-scale data transformation and loading in data lake environments,improving runtime efficiency.
• Implemented serverless data workflows in Apache Airflow, integrating with diverse cloud and database services for dependency management and scheduling.
• Orchestrated complex data workflows in Apache Airflow, integrating with diverse cloud and database services for dependenccy management and scheduling.
• Deployed and managed AWS Databricks environments with DBT integration, enabling data versioned data modeling, quality validation, and modular transformations.
• Leveraged Azure Data Factory and GCP Storage for cross-cloud data ingestion and synchronization,enabling unified analytics across hybrid environments.
• Optimized Pyspark jobs using partitioning, caching, and broadcast joins, improving performance for large-sacle datasets.
• Enforced data security and governance via detailed IAM policies, encryption standards and role-based access control across multi-cloud environmennts.