Join Iris Software, a distinguished IT services company and one of India's top workplaces, and contribute to building award-winning technology solutions. We are dedicated to being a trusted technology partner for enterprises, enabling their transformation across financial services, healthcare, transportation, and professional services. Our work involves complex, mission-critical applications leveraging cutting-edge technologies in application engineering, data and analytics, cloud, DevOps, and more.

At Iris, we believe in empowering our employees. Our philosophy, 'Build Your Future. Own Your Journey.', underscores our commitment to providing you with ownership of your career path and opportunities to shape your success. Experience a culture that values your potential, amplifies your voice, and ensures your work makes a significant impact. Benefit from engaging projects, tailored career development, continuous learning, and mentorship designed for your personal and professional growth.

As a Senior Data Engineer, you will architect and implement scalable data engineering solutions using PySpark and advanced distributed processing frameworks. Responsibilities include defining robust data ingestion, transformation, and processing architectures, and optimizing Snowflake or Delta Lake on Databricks for enterprise-scale data platforms. You will lead the development of high-performance batch and streaming data pipelines, design event-driven architectures with Apache Kafka or Amazon Kinesis, and establish standards for data streaming and scalable processing patterns. Additionally, you will architect workflow orchestration with Apache Airflow or Databricks Workflows, implement comprehensive monitoring and scheduling, and champion data quality, governance, and observability practices.

Further contributions involve designing data engineering solutions based on Lakehouse principles, promoting AI-assisted engineering to boost productivity and quality, and reviewing pipeline designs for adherence to standards. You will troubleshoot complex platform issues, mentor team members on best practices for key technologies, and collaborate with stakeholders for end-to-end data platform delivery.

The ideal candidate possesses expertise in Databricks Workflows, PySpark, Apache Kafka, and Snowflake. A strong understanding of modern Lakehouse architecture, data observability, and platform engineering standards is essential for enhancing scalability, reliability, and operational visibility. We seek individuals who can drive the development of impactful business data products by improving data quality, discoverability, and usability.

Behavioral competencies include demonstrating strong ownership, effective collaboration with diverse teams and stakeholders, and a commitment to quality-focused engineering through proactive validation and continuous improvement. Strong analytical thinking is required for complex data engineering and platform challenges, alongside adaptability in managing evolving technologies and business needs. Excellent communication skills for reporting delivery status, risks, and opportunities, coupled with high attention to detail in architecture, design, testing, and implementation, are crucial for success in this role.