Join Iris Software, a recognized Top 25 Best Workplace in India's IT industry, and shape your career in an award-winning culture that values ambition and talent. As a fast-growing IT services company, we empower our associates to own and drive their success stories.

Our vision is to be the most trusted technology partner for our clients and the preferred destination for top industry professionals. With a global presence across India, the U.S.A., and Canada, we drive technology-enabled transformations for enterprise clients in financial services, healthcare, transportation & logistics, and professional services. We specialize in complex, mission-critical applications, leveraging cutting-edge technologies like Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation.

Design and implement scalable data engineering solutions using PySpark and distributed data processing frameworks. Define robust data ingestion, transformation, and processing architectures aligned with business objectives. Optimize Snowflake or Delta Lake on Databricks 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. Architect workflow orchestration using Apache Airflow or Databricks Workflows, ensuring reliable execution through monitoring and scheduling.

Drive data quality, validation, reconciliation, and governance across all data engineering solutions. Implement Lakehouse architecture principles, data observability, and platform engineering standards to enhance scalability and reliability. Develop business-focused data products by improving data quality, discoverability, usability, and documentation. Promote responsible AI-assisted engineering to boost productivity and quality. Review designs and implementations for adherence to standards, and troubleshoot complex issues through root cause analysis. Mentor team members on best practices and collaborate with stakeholders for end-to-end data platform delivery.

This role requires extensive experience with PySpark, Apache Kafka, Databricks Workflows, and Delta Lake on Databricks. A strong understanding of Snowflake and Apache Airflow for workflow orchestration is essential. Expertise in designing and optimizing scalable data engineering solutions, including batch and streaming pipelines, is crucial. Candidates must be adept at defining data ingestion, transformation, and processing architectures, and establishing data quality and governance practices.

Proven ability to architect event-driven data solutions and implement monitoring and operational controls is expected. A deep understanding of Lakehouse architecture, data observability, and platform engineering principles is required. The candidate should possess strong analytical and problem-solving skills to troubleshoot complex data processing issues. Excellent communication and collaboration skills are necessary to work effectively with various teams and stakeholders. Mentoring junior team members on data engineering best practices is also a key aspect of this role.