- 5+ years of working experience in Snowflake, Databricks, DBT and Python on Azure/GCP/AWS in production environments and hands-on experience in developing moderate to complex ETL/ELT data pipelines

- Strong in Apache Spark and Delta Lake, strong SQL and Python abilities, experience with data engineering and ETL pipelines

- Advanced SQL in Snowflake (CTEs, Views, UDFs, Materialized Views) & DBT

- Partitioning, clustering, and performance tuning in Snowflake

- Streams, Tasks, and Snowpipe for real-time and incremental data pipelines

- Cost and query optimization (e.g., using result cache, pruning, compute credits control)

- DBT-Data Modeling, SQL Mastery,Jinja Templating, Workflow Optimization & Configuration, Data Loading and Cleaning

- Hands on Python

- 3+ years of hands-on experience with Cloud Composer (Airflow) developing DAGs

- 3+ years of hands-on experience with Databricks and the ability to resolve complex SQL query performance issues

- 4+ years of ETL Python development experience; experience parallelizing pipelines a plus

- Demonstrated ability to troubleshoot complex query, pipeline, and data quality issues

- Develop, test, and deploy robust ETL pipelines on Snowflake on MS Azure/AWS/GCP Platform using, but not limited to, Snowflake/dbt/Databricks, Cloud Composer, and Cloud Run to ingest structured/unstructured data from on-prem SQL Server instances into Snowflake Models/Database/Schema

- Implement data validation checks, error handling, and logging to ensure pipeline reliability

- Automate deployment workflows using CI/CD pipelines (GitHub Actions, etc.) and infrastructure-as-code (IaC) tools

- Monitor pipelines via Cloud Logging and Cloud Monitoring, implementing alerting for data latency or quality issuese able to explain the solutions approach on problems