Join an award-winning IT services company recognized as one of India's Top 25 Best Workplaces. At Iris Software, we are a rapidly growing organization dedicated to being our clients' most trusted technology partner and a premier destination for top industry talent. We empower enterprise clients across financial services, healthcare, transportation & logistics, and professional services with cutting-edge technology-enabled transformations.

Our expertise spans complex, mission-critical applications leveraging the latest technologies, including application and product engineering, data & analytics, cloud, DevOps, Data & MLOps, quality engineering, and business automation. We believe in providing a launchpad for growth, where your career journey is owned and shaped by you.

Design and implement scalable data engineering solutions using PySpark and distributed data processing frameworks. Define robust data ingestion, transformation, and processing architectures to meet business and analytical needs. Architect and optimize solutions on platforms like Snowflake or Delta Lake on Databricks for enterprise-scale data platforms. Lead the development of high-performance batch and streaming data pipelines. Design and optimize event-driven data architectures utilizing Apache Kafka or Amazon Kinesis. Establish standards, frameworks, and patterns for scalable data streaming processing. Architect workflow orchestration solutions using tools such as Apache Airflow or Databricks Workflows. Implement comprehensive monitoring, scheduling, and operational controls for reliable pipeline execution. Drive data quality, validation, reconciliation, and governance across all data engineering solutions. Develop data products that enhance data quality, discoverability, usability, and trusted consumption. Promote the responsible use of AI-assisted engineering to boost development productivity and quality. Review data pipeline designs and implementations to ensure adherence to engineering, scalability, and performance standards. Conduct detailed root cause analysis to troubleshoot complex data processing, workflow, and streaming platform issues. Mentor team members on PySpark, Snowflake, Delta Lake, Kafka, Kinesis, Airflow, and data engineering best practices. Collaborate with various teams and stakeholders to ensure end-to-end data platform delivery.

We are seeking experienced engineers with mandatory expertise in PySpark, Apache Kafka, Databricks Workflows, and Delta Lake on Databricks. A strong background in designing scalable data engineering solutions, defining data ingestion and transformation architectures, and optimizing data platforms is essential.

Proficiency in designing and optimizing solutions on Snowflake or Delta Lake on Databricks is required. Candidates should have experience in implementing high-performance batch and streaming data pipelines, along with designing event-driven data architectures using Apache Kafka or Amazon Kinesis.

Experience in architecting workflow orchestration solutions with Apache Airflow or Databricks Workflows is necessary. You should be adept at establishing monitoring, scheduling, and operational controls, and driving data quality, validation, reconciliation, and governance practices. Familiarity with modern Lakehouse architecture principles, data observability, and platform engineering standards is expected.

Strong behavioral competencies including ownership, collaboration, quality focus, analytical thinking, adaptability, effective communication, and attention to detail are highly valued. Candidates should also demonstrate a commitment to continuous improvement and knowledge sharing.