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 our clients' most trusted technology partner and a preferred destination for top industry professionals. With a global team of over 4,300 associates, we drive technology-enabled transformations for enterprise clients across various sectors, leveraging cutting-edge technologies like Data & Analytics, Cloud, and DevOps.

At Iris, we empower our employees through our "Build Your Future. Own Your Journey." philosophy. We cultivate an environment where your potential is recognized, your voice is heard, and your contributions create significant impact. We offer extensive opportunities for career development, continuous learning, mentorship, and engagement with innovative projects, supporting both your personal and professional growth.

As a Senior Engineer, you will be instrumental in designing and implementing scalable data engineering solutions. This includes architecting robust data ingestion, transformation, and processing frameworks using PySpark and distributed systems.

You will be responsible for optimizing data platforms, including Snowflake or Delta Lake on Databricks, to support enterprise-level data needs. You will lead the development of high-performance batch and streaming pipelines, and design event-driven architectures using Apache Kafka or Amazon Kinesis.

Further responsibilities involve establishing monitoring and operational controls for pipeline reliability, driving data quality and governance initiatives, and adhering to Lakehouse architecture principles. You will also promote the use of AI for enhancing development productivity and quality, and mentor team members on best practices.

We are seeking a Senior Engineer with 5-8 years of experience, possessing strong expertise in Databricks Workflows, Apache Spark, Data Quality & Validation, PySpark, SQL, Apache Kafka, and Snowflake. Experience with Azure Databricks is considered an advantage.

Key qualifications include the ability to design scalable data engineering solutions, define data ingestion and transformation architectures, and optimize data platforms. You should be adept at leading the implementation of batch and streaming data pipelines, and designing event-driven data architectures.

Ideal candidates will establish monitoring and operational controls, drive data quality practices, and promote the use of AI-assisted engineering. Strong analytical skills, adaptability to evolving technologies, and effective communication are essential for this role. A collaborative spirit and a commitment to continuous improvement are also highly valued.