What is the Job like?

• Design, develop & maintain data platform services and ETLs for varied use cases such as data extraction, processing, observability and analytics.
• Develop solutions with compliance and security as highest priorities. 
• Design, implement and operate the analytical data warehouse/data lake in support of the business objectives.
• Enable performance monitoring and failure detection of the platform services.
• Contribute to establishing and enforcing coding & operational best practices for the team.
• Execute data architecture solutions and technical troubleshooting with ability to propose new approaches in a fast-paced startup environment.

Who should apply?

• 3-5 years of experience in data engineering.
• Experienced in data modeling and building scalable data/delta lake and data warehouse.
• Strong PySpark and SQL work experience with distributed data processing frameworks.
• Strong knowledge of architecture & internals of Apache Spark with multiple years of hands-on experience.
• Design and implement production grade and complex ETL pipelines on core principles of reliability, scalability and maintainability.
• Understand relational database systems and modeling.
• Familiar with the AWS and Kubernetes environment and resources.
• Knowledge of Airflow and Nifi.
• Experience working with distributed SQL engines like Athena/Trino/Redshift