Snowflake Developer

• Over all 5+ years of experience in IT industry, 3+.years of experience with Snowflake 3+ years of experience in Oracle Sql,Plsql ,DBT ,Apachi Kafka working as a Data engineer and highly motivated individual with proven ability to learn fast and work well under pressure.
• Apache Kafka: A distributed streaming platform used for building real-time data pipelines. 
• Confluent Platform/Cloud: A platform for building and managing Kafka-based streaming data pipelines. 
• Snowflake Cloud Data Warehouse: A cloud-based data warehouse used for analytics and business intelligence. 
• Kafka Connect: A framework for connecting Kafka to external systems, including databases and data warehouses. 
• Confluent Cloud Connectors: Fully managed connectors for various data sources and sinks, including Snowflake. 
• SQL: Used for querying and manipulating data in Snowflake. 
• ksqlDB: A stream processing platform built on top of Kafka. 

Roles & Responsibilities

• Designing and Implementing Data Pipelines:
• Creating and managing the flow of data between Kafka (often used for streaming data) and Snowflake (used for data warehousing and analytics). 
• Kafka Connect and Confluent Cloud Connectors:
• Utilizing Kafka Connect to move data efficiently and effectively, potentially leveraging Confluent's fully-managed connectors for Snowflake. 
• Snowflake Expertise:
• Deep understanding of Snowflake's architecture, data loading, performance optimization, and security best practices. 
• ETL Processes:
• Developing and optimizing Extract, Transform, Load (ETL) processes to move data from various sources into Snowflake via Kafka. 
• Data Modeling and Architecture:
• Designing scalable and efficient data models within Snowflake to support analytical workloads. 
• Monitoring and Troubleshooting:
• Ensuring the reliability and performance of data pipelines, identifying and resolving issues related to data ingestion and processing. 
• Reverse ETL:
• Potentially involved in Reverse ETL, moving data from Snowflake back into operational systems via Kafka. 
• Real-time Analytics:
• Working with streaming data and near real-time analytics use cases, leveraging the combined power of Kafka and Snowflake.