John Abhilash

Project : Alteryx to Snowflake Migration

Client: Zimmer Biomet
Duration: December 2025 – Present
Role: ETL Developer / Data Migration / Data Validation
Technologies: Snowflake, Alteryx, DBT, SQL, Python, Gen AI

• Migrated 100+ Legacy Alteryx workflows to Snowflake, analyzing existing workflow logic and converting transformations into Snowflake SQL and DBT models.
• Created Snowflake views, stored procedures, and other database objects as outputs of migrated workflows based on business requirements.
• Developed Python automation to parse Alteryx XML workflow definitions and generate initial SQL logic, reducing manual effort during workflow conversion.
• Performed source-to-target mapping, transformation analysis, data reconciliation, and validation between existing Alteryx outputs and Snowflake results.
• Worked directly with multiple business user groups to understand requirements, clarify transformation logic, and resolve data-related issues during migration.
• Handled UAT activities with business users, including test coordination, output validation, defect analysis, and resolution of identified issues.
• Managed deployment of migrated workflows and database objects into business-specific Snowflake schemas across environments.
• Performed end-to-end validation of migrated workflows, including record counts, duplicate checks, null checks, business rules, and output reconciliation.
• Troubleshot discrepancies between Alteryx and Snowflake results by analyzing joins, filters, data types, calculations, and transformation logic.
• Used DBT for modular transformation development, testing, and documentation of migrated logic.
• Supported post-deployment validation and production issues, coordinating with business and technical teams to ensure successful migration.
• Managed the migration lifecycle across multiple business groups, from Alteryx workflow analysis → SQL conversion → Snowflake development → UAT → deployment → production validation.

Project : Enterprise Data Lake & Snowflake Migration

• Designed scalable pipelines to ingest data from AWS RDS and Salesforce into Snowflake via S3, processing approximately 10 GB/day across 40+ tables.
• Developed ETL/ELT workflows using DBT and Snowflake, following a layered Raw → Staging → Curated architecture, improving transformation reliability and reducing manual SQL effort by 60%.
• Developed reusable DBT transformation models, parameterized logic, and environment-specific configurations to support scalable data processing.
• Built a real-time ingestion framework using Snowpipe, S3 Events, and External Tables, reducing ingestion latency by approximately 40%.
• Implemented SCD Type 1 and Type 2 using Snowflake Streams, Tasks, and DBT models, avoiding unnecessary full-refresh processing.
• Optimized Snowflake query performance using clustering, micro-partition pruning, Materialized Views, and result caching, improving query performance by 30–35%.
• Used Zero-Copy Cloning and Time Travel for testing and data recovery, reducing environment provisioning time by approximately 90%.
• Automated DBT job execution and Snowflake deployments using GitHub Actions and CI/CD pipelines.
• Implemented data governance using RBAC, masking policies, and access controls for sensitive data.

Project : API-based Data Ingestion & Snowflake Processing

• Built automated ingestion pipelines using AWS Lambda (Python) to fetch JSON/XML/CSV API data and store approximately 5–8 GB/day in Amazon S3.
• Implemented Snowpipe + SQS to enable near real-time ingestion into the Snowflake RAW zone.
• Developed ELT workflows using DBT and Snowflake following a Raw → Staging → Curated architecture for deduplication, schema testing, documentation, and modular SQL transformations.
• Applied JSON flattening using LATERAL FLATTEN to transform semi-structured data into analytics-ready tables.
• Implemented data security using Row-Level Security, Column Masking, and RBAC policies.
• Monitored pipelines using AWS CloudWatch and Snowflake pipe notifications, reducing incident response time by approximately 25%.
• Optimized Snowflake queries using partition pruning, result caching, and clustering, improving query performance by approximately 20–25%.
• Automated DBT and Snowflake deployment pipelines using GitHub CI/CD, reducing manual intervention by approximately 40%.