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Snowflake Developer

2 weeks ago

Bengaluru, Karnataka, India Softtek Full time US$ 1,50,000 - US$ 2,00,000 per year
  • Over all 5+ years of experience in IT industry, 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.