Key Responsibilities

• Set up and manage Kafka clusters and Kafka Connect to handle real-time data flows.
• Monitor and support streaming data pipelines, ensuring reliability, scalability, and performance.
• Build and maintain data models in Snowflake to support reporting and analytics needs.
• Optimize Snowflake usage for scalability and performance across teams.
• Design and develop ETL workflows using AWS Glue to process both structured and unstructured data.
• Utilize the AWS Glue Data Catalog to organize metadata and support querying through Amazon Athena.
• Implement best practices for organizing and securing data stored in AWS S3.
• Create and manage data workflows in Apache Airflow, ensuring timely and reliable ETL execution.
• Troubleshoot failures and continuously improve orchestration processes.
• Support data quality, governance, and access control initiatives to maintain trust in data assets.
• Collaborate with data analysts, data scientists, and business stakeholders to deliver reliable and usable data.
• Stay up to date with evolving tools and best practices in cloud data engineering.

Candidate Profile

• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field.
• 2–3 years of hands-on experience in data engineering, with a focus on cloud platforms.
• Practical experience with:
• Kafka for real-time data processing.
• Snowflake for data warehousing.
• AWS Glue, Athena, and S3 for data processing and storage.
• Apache Airflow for workflow orchestration.
• Proficiency in SQL, Python, and shell scripting.
• Solid understanding of data modeling and cloud security principles.

Preferred Qualifications

• Exposure to Microsoft Azure or Google Cloud Platform (GCP).
• Experience with CI/CD tools such as Jenkins or Git.
• Familiarity with Docker or basic containerization concepts.