Join Iris Software, recognized among India's Top 25 Best Workplaces in IT, and contribute to your most impactful career work. We are a rapidly expanding IT services company committed to being a trusted technology partner and the preferred employer for top professionals. Our diverse team collaborates across India, the USA, and Canada, empowering enterprise clients with technology-driven transformations in finance, healthcare, transportation, and professional services. We specialize in complex, mission-critical applications utilizing cutting-edge technologies in Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation.

Lead the design and implementation of robust, scalable data engineering solutions. Develop advanced architectures for data ingestion, transformation, and processing, aligning with business and analytical needs.

Architect and optimize enterprise-scale data platforms using Snowflake or Delta Lake on Databricks. Drive the creation of high-performance batch and streaming data pipelines, including event-driven architectures with Apache Kafka or Amazon Kinesis.

Define standards for data streaming, integration, and scalable processing. Design and implement workflow orchestration solutions using Apache Airflow or Databricks Workflows, ensuring reliable pipeline execution through monitoring and operational controls.

Champion data quality, validation, and governance practices. Implement Lakehouse architecture principles, data observability, and platform engineering standards to enhance scalability and operational visibility.

Drive the development of business-focused data products by improving data quality, discoverability, and usability. Leverage AI-assisted engineering capabilities to boost productivity and quality in development and testing. Review designs and implementations to ensure adherence to engineering, scalability, and performance standards. Troubleshoot complex data issues through root cause analysis and mentor team members on best practices.

This role requires strong expertise in PySpark and modern distributed data processing frameworks. Experience with Amazon Kinesis, Delta Lake on Databricks, Databricks Workflows, Snowflake, Apache Kafka, and Apache Airflow is essential.

Key responsibilities include designing scalable data engineering solutions, defining data ingestion and transformation architectures, and optimizing data platforms. You will lead the implementation of high-performance data pipelines, design event-driven architectures, and establish data streaming standards.

Candidates should be adept at architecting workflow orchestration solutions, implementing monitoring and operational controls, and driving data quality and governance. Experience with Lakehouse architecture principles, data observability, and platform engineering standards is crucial.

We are seeking individuals who can drive development of data products, promote responsible use of AI-assisted engineering, and review data pipeline designs for adherence to standards. Strong analytical skills, effective communication, and the ability to troubleshoot complex data processing issues are vital. Mentoring team members and collaborating with stakeholders are also key aspects of this role.