Join Iris Software, recognized as one of India's Top 25 Best Workplaces in the IT industry. We are a rapidly expanding IT services company dedicated to being a trusted technology partner and a platform for top professionals to achieve their full potential. With a global presence across India, the U.S.A., and Canada, we empower enterprise clients in financial services, healthcare, transportation & logistics, and professional services through technology-enabled transformations. Our expertise spans complex, mission-critical applications utilizing cutting-edge technologies in application and product engineering, data & analytics, cloud, DevOps, MLOps, quality engineering, and business automation.

Develop and manage robust data pipelines and processing solutions using PySpark. Design and implement data ingestion, transformation, and workflow processes for analytical and operational needs. Build enterprise data platforms utilizing Snowflake or Delta Lake on Databricks. Create batch and near real-time data processing pipelines. Implement event-driven data integration with Apache Kafka or Amazon Kinesis. Develop scalable data streaming workflows. Configure workflow orchestration using Apache Airflow or Databricks Workflows. Monitor and support scheduled data jobs for operational stability. Perform data validation and quality checks. Leverage AI-assisted engineering tools for enhanced efficiency. Support the development of reliable data products, ensuring data quality and accessibility. Troubleshoot data pipeline and workflow issues. Assist with testing, deployment, and production support. Maintain technical documentation and operational procedures. Collaborate with cross-functional teams to support data platform and analytics initiatives.

This role requires proficiency in PySpark, Apache Kafka, Databricks Workflows, Delta Lake on Databricks, and Snowflake. Candidates should demonstrate strong ownership, accountability, and a quality-first mindset. Excellent analytical and problem-solving skills are essential for resolving data engineering challenges. Adaptability to learn new technologies and effective communication of progress, risks, and dependencies are key. Experience with event-driven architectures, workflow orchestration tools like Apache Airflow, and a commitment to data quality and documentation are highly valued.