Manoj Kumar V M

Project 1: Enterprise Data Warehouse Migration to Snowflake
Role: Snowflake Developer / Data Engineer
Environment: Snowflake, SQL, AWS S3, Snowpipe, Streams, Tasks, Python, ETL Tools

• Analyzed existing legacy data warehouse schemas, ETL workflows, and data models to design an optimized Snowflake architecture.
• Migrated large-scale structured data from on-premise databases to Snowflake using staging in AWS S3.
• Designed and implemented Snowflake databases, schemas, tables, views, and stages for efficient data storage and processing.
• Built automated data ingestion pipelines using Snowpipe for continuous loading of data from cloud storage.
• Implemented Streams and Tasks for incremental data processing and change data capture (CDC).
• Developed complex SQL transformations and stored procedures to streamline data processing and implement business logic.
• Optimized query performance using clustering keys, micro-partitioning awareness, and query profiling.
• Implemented Time Travel and Fail-safe features for data recovery and auditing.
• Monitored and managed Snowflake warehouses for cost optimization and performance tuning.
• Designed end-to-end data migration strategy including data extraction, staging, transformation, and validation processes.
• Implemented multi-layer data architecture (Raw, Staging, and Curated layers) for structured data processing.
• Created external stages integrating AWS S3 with Snowflake for efficient data ingestion.
• Developed metadata-driven ETL frameworks to standardize data loading and transformation processes.
• Implemented data quality validation checks during migration to ensure data consistency between source and Snowflake.
• Used COPY INTO commands and bulk loading techniques for high-performance data ingestion.
• Implemented role-based access control (RBAC), masking policies, and secure views to ensure data security and governance.
• Automated data pipeline scheduling using Snowflake Tasks to reduce manual intervention.
• Performed query performance monitoring using Snowflake Query Profile and History.
• Optimized storage and compute costs by configuring warehouse auto-suspend and auto-resume features.
• Implemented data deduplication and incremental loading strategies using Streams.
• Conducted data reconciliation and validation reports to ensure migration accuracy.

Project 2: Cloud Data Pipeline Development and Analytics Platform
Role: Snowflake Developer
Environment: Snowflake, SQL, Snowpipe, Streams, Tasks, AWS S3, Python, ETL Tools

• Designed and developed data pipelines in Snowflake to ingest data from various sources such as application databases, APIs, and flat files.
• Created internal and external stages for loading structured and semi-structured data (CSV, JSON).
• Developed incremental data processing pipelines using Streams and Tasks for CDC-based transformations.
• Built fact and dimension tables following star schema data modeling for analytical workloads.
• Developed complex SQL queries, views, and transformations to support business reporting requirements.
• Implemented data validation rules and quality checks, enhancing data reliability for analytics and reporting.
• Collaborated with data analysts and BI teams, enabling effective reporting and dashboard development.
• Maintained role-based access control (RBAC) to ensure secure data access.
• Designed scalable ELT pipelines for continuous ingestion and transformation of enterprise data.
• Implemented Snowflake staging architecture (Internal and External Stages) for efficient data ingestion.
• Automated data loading pipelines using Snowpipe for near real-time data ingestion.
• Built incremental data transformation frameworks using Streams and Tasks to process change data efficiently.
• Implemented data partitioning and clustering strategies to improve query performance for analytical workloads.
• Developed data marts optimized for BI reporting and dashboard performance.
• Created dynamic views and materialized views to support high-performance analytics queries.
• Implemented data validation frameworks to detect anomalies and missing records.
• Developed Python scripts for automation of data ingestion and monitoring processes.
• Integrated Snowflake with BI tools for reporting and business insights generation.
• Implemented data security policies including role hierarchy and privilege management.
• Monitored data pipeline execution and troubleshooting failures in ETL workflows.
• Designed audit logging and monitoring processes for pipeline reliability.