1. Designing and implementing scalable data pipelines 1. Building and managing data warehouses and data lakes 1. Ensuring data quality and implementing data management best practices 1. Optimizing data storage and retrieval processes 1. Collaborate closely with data scientists, analysts, and product teams to support analytics and machine learning initiatives. 1. CI/CD orchestration and automation tools: Experience with tools such as Jenkins, GitHub etc. 1. Monitor and tune Snowflake query performance, warehouse usage, and credit consumption. 1. Collaborate closely with data scientists, analysts, and product teams to support analytics and machine learning initiatives. 1. Design and enforce row-level access policies and dynamic masking in Snowflake for sensitive data fields (PII, financials). 1. Enabled data sharing with external teams using secure shares and reader accounts while maintaining strict RBAC controls. 1. Experience with ETL /Scheduler tools. 1. Strong interpersonal, written, and verbal communication skills to interact effectively across teams and stakeholders. 1. Designing semantic layers, aggregate tables, and data models (Star/Snowflake) to support scalable, governed, and business-friendly analytics architecture.