Description

Designed and implemented scalable data architectures and ELT/ETL pipelines in Snowflake using Python, SQL, dbt, and Airflow to support analytics and reporting needs. Optimized warehouse performance through clustering keys, caching strategies, and efficient warehouse sizing for cost and speed improvements. Built robust data ingestion frameworks using Snow pipe, Kafka, and ETL tools to handle diverse data formats (JSON, XML, CSV). Implemented strong security and governance practices, including RBAC, masking policies, and secure data sharing. Collaborated with stakeholders, architects, and modelers to translate business requirements into reliable, high-quality data solutions. Location: Remote. Shift timings: Normal (Day Shift). End of curated JD. Job Description: Data Engineer designs, builds, and optimizes scalable data pipelines and architectures within the Snowflake cloud platform. Key responsibilities include creating ETL/ELT workflows (using tools like Airflow, dbt, Python), optimizing performance via clustering keys, implementing security (RBAC), and managing data integration to ensure high-quality data access for analytics. Pipeline Development: Develop and maintain efficient ELT/ETL pipelines using Snowflake SQL, Python, or Javascript. Data Modeling & Architecture: Design secure, scalable data models and schemas within Snowflake. Performance Optimization: Optimize Snowflake data warehouse performance, including warehouse sizing, caching, and clustering keys. Data Ingestion: Implement data ingestion using Snow pipe, Kafka, or ETL tools for various file formats (JSON, XML, CSV). Security & Compliance: Implement data security models, Role-Based Access Control (RBAC), masking policies, and data sharing capabilities. Automation: Create re-usable frameworks and automate data processes using Tasks and Streams

Key Skills

Education

Bachelor's degree