Join a dynamic team at 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 empowering our clients with cutting-edge technology solutions and offering our professionals a platform for significant career growth. Our vision is to be the premier technology partner for enterprises, fostering an environment where top talent can achieve their full potential.

With a global presence across India, the U.S.A., and Canada, we specialize in transforming businesses through advanced application and product engineering, data and analytics, cloud solutions, DevOps, Data & MLOps, quality engineering, and business automation. We tackle complex, mission-critical applications using the latest technologies to drive client success.

At Iris, we believe in "Build Your Future. Own Your Journey." This philosophy underscores our commitment to providing employees with career ownership and opportunities for development. We cultivate a culture that values your contributions, amplifies your voice, and ensures your work has a tangible impact. Through challenging projects, tailored career development plans, continuous learning, and mentorship, we support your professional and personal evolution.

- Design and implement scalable data engineering solutions using PySpark and advanced distributed data processing frameworks. - Define comprehensive data ingestion, transformation, and processing architectures aligned with business objectives. - Architect and optimize Snowflake or Delta Lake on Databricks for enterprise-scale data platforms. - Lead the development of high-performance batch and real-time data pipelines. - Design event-driven data architectures leveraging Apache Kafka or Amazon Kinesis. - Establish robust data streaming standards, integration frameworks, and scalable processing patterns. - Architect workflow orchestration using Apache Airflow or Databricks Workflows. - Implement monitoring, scheduling, and operational controls for reliable pipeline execution. - Drive data quality, validation, reconciliation, and governance across data engineering solutions. - Develop data products that enhance data quality, discoverability, usability, and trusted consumption. - Promote responsible AI-assisted engineering to boost productivity and quality. - Review and ensure data pipeline designs meet engineering, scalability, and performance standards. - Conduct root cause analysis for complex data processing, workflow, and streaming issues. - Mentor team members on best practices in PySpark, Snowflake, Delta Lake, Kafka, Kinesis, Airflow, and data engineering.

- Proven expertise in PySpark, Apache Kafka, Databricks Workflows, Delta Lake on Databricks, and Snowflake is mandatory. - Strong ability to design scalable data engineering solutions and define data processing architectures. - Experience in architecting and optimizing enterprise-scale data platforms using Snowflake or Delta Lake. - Proficiency in leading the implementation of high-performance batch and streaming data pipelines. - Experience designing event-driven data architectures with Apache Kafka or Amazon Kinesis. - Ability to define data streaming standards and integration frameworks. - Experience in architecting workflow orchestration solutions, preferably with Apache Airflow or Databricks Workflows. - Skills in establishing monitoring, scheduling, and operational controls for data pipelines. - Demonstrated capability to drive data quality, validation, reconciliation, and governance practices. - Familiarity with Lakehouse architecture principles, data observability, and platform engineering standards. - Experience in developing business-focused data products and improving data usability and documentation. - Ability to promote responsible AI-assisted engineering practices for development. - Strong analytical thinking and problem-solving skills for complex data platform challenges. - Excellent collaboration and communication skills for effective stakeholder engagement and delivery. - Adaptability to manage evolving technologies and business requirements. - Experience mentoring team members on data engineering best practices.