Join Iris Software, recognized as one of India's Top 25 Best Workplaces in the IT industry. We are a rapidly growing IT services company committed to being a trusted technology partner and a platform for top professionals to realize their full potential.

With over 4,300 associates across India, the U.S.A., and Canada, we empower enterprise clients in financial services, healthcare, transportation & logistics, and professional services. Our expertise spans complex, mission-critical applications leveraging cutting-edge technologies in Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation.

Design and implement scalable data engineering solutions using PySpark and modern distributed data processing frameworks. Define robust data ingestion, transformation, and processing architectures aligned with business objectives. Architect and optimize Snowflake or Delta Lake on Databricks for enterprise-scale data platforms. Lead the development of high-performance batch and streaming data pipelines. Design and optimize event-driven data architectures leveraging Apache Kafka or Amazon Kinesis. Establish monitoring, scheduling, and operational controls for reliable pipeline execution. Drive data quality, validation, reconciliation, and governance across data engineering solutions. Promote the responsible use of AI-assisted engineering to enhance development productivity and quality. Review data pipeline designs to ensure adherence to engineering, scalability, and performance standards. Troubleshoot complex data processing, workflow, and streaming platform issues with detailed root cause analysis. Mentor team members on PySpark, Snowflake, Kafka, and data engineering best practices. Collaborate with stakeholders to support end-to-end data platform delivery.

Mandatory skills include proficiency in Databricks Workflows, PySpark, Apache Kafka, and Snowflake. Experience with designing scalable data engineering solutions and defining data ingestion/transformation architectures is essential. Strong ability to design and optimize Snowflake or Delta Lake on Databricks solutions for enterprise-scale data platforms. Expertise in implementing high-performance batch and streaming data pipelines, and designing event-driven architectures using Apache Kafka or Amazon Kinesis. Skills in architecting workflow orchestration solutions (Apache Airflow or Databricks Workflows) and establishing monitoring/scheduling controls are required. A focus on data quality, validation, reconciliation, and governance is crucial. Demonstrated ownership, effective collaboration with teams and stakeholders, and strong analytical thinking are key behavioral competencies. Ability to adapt to evolving technologies and manage complex data engineering challenges is expected.