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 and a premier destination for industry professionals to achieve their full potential.

Our global team of over 4,300 associates leverages cutting-edge technologies like high-value application and product engineering, data and analytics, cloud, DevOps, and AI/ML to drive transformation for enterprise clients across various sectors. We foster an award-winning culture that genuinely values talent and ambition, offering a launchpad for significant career growth.

Design and implement scalable data engineering solutions utilizing PySpark and modern distributed processing frameworks. Architect robust data ingestion, transformation, and processing pipelines aligned with business objectives. Develop and optimize Snowflake or Delta Lake on Databricks solutions for enterprise-scale data platforms. Lead the creation of high-performance batch and streaming data pipelines, incorporating event-driven architectures with Apache Kafka or Amazon Kinesis. Establish comprehensive monitoring, scheduling, and operational controls to ensure reliable pipeline execution. Drive data quality, validation, and governance practices, adhering to Lakehouse architecture principles and platform engineering standards. Promote the responsible use of AI for enhanced development productivity and solution quality. Review pipeline designs to ensure adherence to engineering, scalability, and performance standards, and troubleshoot complex issues through root cause analysis. Mentor team members on key technologies and data engineering best practices, and collaborate with stakeholders for end-to-end platform delivery.

We seek a Senior Engineer with 5-8 years of experience in data engineering. Essential skills include proficiency in Databricks Workflows, Apache Spark, Data Quality & Validation, PySpark, SQL, Apache Kafka, and Snowflake. Azure Databricks is a valuable additional skill.

This role requires strong ownership, effective collaboration with diverse teams and business stakeholders, and a commitment to quality-focused engineering through proactive validation and optimization. Applicants should possess robust analytical thinking, adaptability to evolving technologies, and clear communication skills regarding delivery status, risks, and opportunities. A keen attention to detail in architecture, design, testing, and implementation is crucial, alongside a drive for continuous improvement and knowledge sharing. Balancing scalability, performance, reliability, and business priorities is key, while promoting innovation through modern practices and AI-assisted development.