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. Our vision is to empower top industry professionals to reach their full potential within an award-winning culture that values talent and ambition. We support enterprise clients across financial services, healthcare, transportation & logistics, and professional services with cutting-edge technology solutions.

Design and implement scalable data engineering solutions using PySpark and distributed data processing frameworks. Architect robust data ingestion, transformation, and processing pipelines, optimizing Snowflake or Delta Lake on Databricks for enterprise-scale platforms. Lead the development of high-performance batch and streaming data pipelines, including event-driven architectures using Amazon Kinesis or Apache Kafka. Develop workflow orchestration solutions with Apache Airflow or Databricks Workflows, ensuring reliable pipeline execution through robust monitoring and scheduling. Drive data quality, validation, reconciliation, and governance practices, applying Lakehouse architecture principles and data observability.

Advance the development of business-focused data products by enhancing data quality, discoverability, and usability. Promote the responsible use of AI-assisted engineering for improved productivity and quality. Review data pipeline designs and implementations to ensure adherence to engineering, scalability, and performance standards. Troubleshoot complex data processing and platform issues through detailed root cause analysis. Mentor team members on PySpark, Snowflake, Delta Lake, Kafka, Kinesis, Airflow, and data engineering best practices. Collaborate with diverse teams and stakeholders to ensure seamless end-to-end data platform delivery.

Demonstrate strong ownership and drive excellence in data engineering practices. Collaborate effectively with various teams and business stakeholders to ensure smooth project delivery. Promote quality-focused engineering through proactive validation, optimization, and continuous improvement. Apply strong analytical thinking to effectively evaluate complex data engineering and platform challenges. Essential skills include Databricks Workflows, Delta Lake on Databricks, Snowflake, PySpark, and Amazon Kinesis. Familiarity with Apache Kafka, Apache Airflow, data quality, data governance, Lakehouse architecture, Python, and SQL is also crucial.