Prasanth B

◆ Led a large-scale migration from AWS Redshift to Snowflake, successfully transitioning over 500 tables with high data quality and end-to-end ownership.
◆ Designed and developed robust ELT pipelines using dbt, implementing complex business logic, reusable macros, UDFs, and data quality tests.
◆ Built and optimized data pipelines, integrating multiple sources such as Fivetran and Salesforce into the data warehouse.
◆ Developed custom Python-based frameworks for handling complex and semi-structured (JSON) data, with validation mechanisms.
◆ Contributed to the implementation of the Medallion Architecture (Bronze, Silver, Gold layers) for scalable and analytics-ready data models.
◆ Ensured strong data governance practices using Atlan, including lineage tracking, classification, and column-level security.

◆ Orchestrated and monitored workflows using Airflow; implemented observability using Splunk.
◆ Collaborated with cross-functional teams to deliver high-quality data solutions within defined timelines.
◆ Provided quick resolution for AWS data warehouse-related issues, and ensured platform stability.
◆ Demonstrated strong expertise in finance data handling, with secure and compliant data practices.

◆ Ensured data quality through rigorous testing, validation, and monitoring of all data assets, minimizing inaccuracies and inconsistencies.

◆ Reengineered existing ETL workflows to improve performance by identifying bottlenecks and optimizing code accordingly.

◆ Championed the adoption of agile methodologies within the team, resulting in faster delivery times and increased collaboration among team members.

◆ Participated in strategic planning sessions with stakeholders to assess business needs related to data engineering initiatives.

◆ Built and maintained highly scalable data pipelines using AWS and Apache Spark for processing large datasets.
Designed and implemented ETL pipelines using AWS Glue to transform raw JSON data into Parquet across Medallion layers.
◆ Developed dimension models with Slowly Changing Dimensions (SCD) for efficient analytical querying in Redshift.
◆ Processed large-scale datasets using PySpark on AWS EMR/EC2 with optimized transformations and performance tuning.
◆ Strong understanding of Spark architecture including joins optimization, partitioning, bucketing, caching, and memory tuning.
◆ Performed end-to-end ETL testing, including data validation, regression testing, and performance benchmarking.
◆ Managed large datasets using advanced SQL (CTEs, temp tables) and multiple file formats (CSV, JSON).
◆ Configured monitoring and alerting for AWS Glue jobs using CloudWatch.
◆ Contributed to CI/CD pipelines using Jenkins and version control using Git.
◆ Conducted code reviews, troubleshooting, and ensured high-quality, reliable data pipelines.
◆ Created documentation and supported teams with best practices and technical guidance.

◆ Developed custom scripts for automating repetitive tasks, freeing up valuable time for team members to focus on more strategic initiatives.