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 committed to being our clients' most trusted technology partner. With a global presence and over 4,300 associates, we empower enterprise clients across diverse sectors like financial services, healthcare, and logistics through cutting-edge technology solutions.

At Iris, we believe in fostering a culture where your potential is valued, and your contributions make a real impact. Our 'Build Your Future. Own Your Journey.' philosophy ensures that every role serves as a launchpad for professional growth, offering continuous learning, mentorship, and ownership of your career path.

This senior role involves designing and implementing scalable data engineering solutions using PySpark and distributed data processing frameworks. You will architect robust data ingestion, transformation, and processing pipelines, optimizing for enterprise-scale data platforms with technologies like Snowflake or Delta Lake on Databricks.

Key responsibilities include leading the development of high-performance batch and streaming data pipelines, designing event-driven architectures with Apache Kafka, and establishing comprehensive monitoring and control mechanisms for reliable pipeline execution. You will also drive data quality, governance, and promote the responsible use of AI-assisted engineering for enhanced productivity and quality. This role requires troubleshooting complex issues and mentoring team members on best practices.

This position requires a strong command of mandatory skills including Databricks Workflows, PySpark, Apache Kafka, and Delta Lake on Databricks. Experience with Snowflake, Amazon Kinesis, and Apache Airflow is also essential for architecting workflow orchestration and streaming solutions.

We are seeking a candidate who demonstrates strong ownership, collaborates effectively with diverse teams and stakeholders, and applies analytical thinking to complex data engineering challenges. A quality-focused engineering approach with proactive validation and continuous improvement is highly valued. The role also involves driving data products by enhancing data quality, discoverability, and usability across analytical platforms.