Prakash

AWS Data Engineer with 2+ years of experience in designing, developing, and supporting scalable data engineering solutions on the AWS cloud. Experienced in building robust ETL and ELT pipelines using sql, python, and pyspark to process structured and semi-structured data from multiple enterprise data sources. Proficient in Amazon S3, AWS Glue, AWS Lambda, Amazon Redshift, Snowflake, Apache Airflow, Amazon Athena, AWS IAM, Amazon RDS, and Amazon CloudWatch for developing secure, reliable, and high-performance data platforms.

Hands-on experience in data ingestion, data integration, data transformation, data migration, data validation, workflow orchestration, batch processing, metadata management, data quality, and cloud migration projects within the investment banking domain. Skilled in loading curated datasets into Snowflake as an enterprise data warehouse and Amazon Redshift for reporting, analytics, and business intelligence. Strong understanding of sql optimization, performance tuning, query optimization, dimensional modeling, star schema, fact and dimension tables, and data reconciliation to deliver efficient analytical solutions.

Experienced in orchestrating end-to-end workflows using Apache Airflow, implementing secure access with AWS IAM, monitoring pipelines through Amazon CloudWatch, and collaborating with cross-functional teams in Agile and Scrum environments to deliver scalable cloud-based data solutions aligned with business requirements.

AWS Data Engineer - FINTECH SOFTWARE SOLUTIONS

(2024-06 - 2025-03)

• Led the migration of financial data from multiple on-premises systems into AWS by consolidating trade, settlement, reference, and client data into a centralized cloud platform.
• Engineered a metadata-driven ingestion framework using AWS Glue to standardize data extraction from heterogeneous sources, reducing manual migration effort and improving consistency.
• Implemented data mapping, schema harmonization, and data integration across disparate financial systems to establish a unified target data model for enterprise reporting.
• Established automated data reconciliation and data validation processes to verify migrated datasets against source systems, ensuring data integrity, accuracy, and compliance with business rules.
• Executed full-load, incremental migration, production cutover, and post-migration validation, ensuring minimal downtime and seamless transition to the AWS cloud environment.
• Optimized migrated datasets through partitioning, Parquet conversion, and performance tuning in Amazon S3 before loading curated data into Snowflake and Amazon Redshift for reporting and analytics.
• Collaborated with application teams, DBAs, cloud engineers, and business stakeholders to resolve migration issues, monitor workflows using Amazon CloudWatch, and deliver successful cloud migration milestones following Agile methodology.

AWS Data Engineer - FINTECH SOFTWARE SOLUTIONS - Chennai

(2025-03)

• Developed scalable ETL pipelines using sql, python, and pyspark to extract, transform, and load investment banking trade, customer, and transaction data from multiple data sources into the AWS cloud.
• Built and maintained cloud-based data processing workflows using amazon s3, aws glue, and aws lambda for data ingestion, transformation, validation, and automated processing of structured and semi-structured datasets.
• Designed and optimized pyspark jobs for data cleansing, enrichment, aggregation, and business rule implementation to deliver high-quality datasets for downstream analytics and reporting.
• Loaded curated datasets into snowflake as the enterprise data warehouse and into amazon redshift to support business reporting, dashboard development, and analytical workloads with optimized sql queries.
• Orchestrated and monitored end-to-end data pipelines using apache airflow, while utilizing amazon cloudwatch for workflow monitoring, job scheduling, logging, alerting, and issue resolution.
• Implemented secure access control using aws iam roles and policies, collaborated with data analysts, business stakeholders, and development teams to ensure reliable data availability, governance, and compliance with organizational standards.
• Performed sql performance tuning, data validation, troubleshooting, and pipeline optimization across AWS services, contributing to scalable, reliable, and maintainable cloud data engineering solutions supporting investment banking operations.

BE - Electronic and Communication Engineering - Jeppiaar Engineering College (2020 - 2024) Chennai