Senior Full Stack Developer

● Designed Snowflake as the core enterprise data warehouse with clustering, materialized views, and Snowpark for high performance, scalability, and cost efficiency. ● Built and maintained end-to-end ELT pipelines using Snowflake, Snowpark (Python), PySpark, dbt, and Airbyte, following Medallion Architecture (Bronze, Silver, Gold). ● Developed Python scripts and Snowflake UDFs for reusable transformations, data cleansing, and standardizing business logic. ● Built and supported real-time pipelines with Apache Kafka and Kafka Connect for APIs, IoT, and transactional data ingestion. ● Implemented CDC in Snowflake using streams, tasks, Python UDFs, and stored procedures for near real-time synchronization. ● Automated ELT workflows with dbt, layered models, tests, documentation, and CI/CD via GitHub Actions

● Assisted in building ETL pipelines using Snowflake, Snowpark (Python), PySpark, dbt, Airbyte, and AWS S3, following Medallion Architecture (Bronze, Silver, Gold). ● Wrote Python scripts and Snowflake UDFs to standardize business logic and support transformations. ● Supported pipeline orchestration with Apache Airflow and Azure Data Factory (ADF), monitoring jobs, troubleshooting failures, and coordinating ingestion from on-prem and cloud systems. ● Helped implement dbt tests, documentation, and automated ELT workflows to ensure high-quality, analytics-ready datasets. ● Queried and validated data in AWS S3 and Athena for reporting and ad-hoc analysis. ● Designed and delivered scalable batch and streaming pipelines using Python, PySpark, and SQL, integrating Apache Kafka and Kafka Connect for real-time ingestion from APIs, IoT, and transactional sources. ● Implemented CDC in Snowflake using streams, tasks, Python UDFs, and stored procedures for incremental loads. ● Executed large-scale transformations in PySpark, tuning performance with partitioning, caching, and adaptive query execution. tuning performance with part ● Designed data models and optimized queries in Snowflake, leveraging Snowpark for advanced in-warehouse processing. ● Architected AWS S3 data lakehouse solutions with lifecycle management to Glacier, supporting raw, curated, and enriched layers. ● Automated pipeline build, test, and deployment using GitHub Actions and Azure DevOps. ● Partnered with data scientists and analysts to convert business requirements into production-grade datasets for reporting and ML use cases. ● Monitored pipeline performance and optimized PySpark, SQL, and Snowflake workloads to improve freshness and reduce costs. ● Strengthened data quality with validation rules, schema enforcement, and logging frameworks across the platform.