Join Iris Software, recognized as one of India's Top 25 Best Workplaces in the IT industry. We are seeking a Senior Data Engineer to contribute to cutting-edge technology solutions across financial services, healthcare, transportation, and professional services.

At Iris, we believe in empowering our employees, offering a launchpad for growth with our "Build Your Future. Own Your Journey." philosophy. You'll work on complex, mission-critical applications utilizing advanced technologies like Data & Analytics, Cloud, DevOps, and Data & MLOps. This is an opportunity to be part of an award-winning culture that values your talent and ambitions.

Design and implement scalable data engineering solutions using PySpark and modern distributed data processing frameworks. Define robust data ingestion, transformation, and processing architectures. Architect and optimize Delta Lake on Databricks or Snowflake solutions for enterprise-scale data platforms. Lead the development of high-performance batch and streaming data pipelines, including event-driven architectures with Apache Kafka or Amazon Kinesis.

Develop and maintain monitoring, scheduling, and operational controls for reliable pipeline execution. Drive data quality, validation, and governance practices. Design solutions adhering to Lakehouse architecture principles and data observability standards. Enhance business-focused data products to improve data quality, discoverability, and usability across analytical platforms.

Expertise in PySpark, Apache Kafka, Databricks Workflows, and Delta Lake on Databricks is mandatory. Proficiency with AWS CLI is also required.

Key responsibilities include designing scalable data engineering solutions, defining data ingestion and transformation architectures, and optimizing data platforms. You will lead the implementation of batch and streaming data pipelines, architect event-driven architectures, and establish monitoring and control mechanisms for pipeline execution. Driving data quality and governance practices is crucial. Experience with modern Lakehouse architecture principles and data observability is essential. Mentoring team members and collaborating with stakeholders are also key aspects of this role.