Join Iris Software as a Senior Data Engineer and contribute to India’s Top 25 Best Workplaces in the IT industry. We are a fast-growing IT services company focused on enabling enterprise clients through technology-enabled transformation across various sectors.

Our vision is to be a trusted technology partner and a preferred employer for top professionals. With a global presence, we specialize in complex, mission-critical applications using cutting-edge technologies in Application & Product Engineering, Data & Analytics, Cloud, DevOps, and more.

At Iris, your role is a launchpad for growth, offering ownership and shaping your career journey. We foster a culture that values potential, encourages voices, and drives real impact through cutting-edge projects, personalized development, and mentorship.

Architect and implement scalable data engineering solutions, focusing on PySpark and distributed data processing frameworks. Design robust data ingestion, transformation, and processing architectures to meet business and analytical objectives.

Optimize Snowflake or Delta Lake on Databricks for enterprise-scale data platforms and lead the development of high-performance batch and streaming data pipelines. Design event-driven data architectures using Apache Kafka or Amazon Kinesis, establishing standards for streaming, integration, and processing patterns.

Architect workflow orchestration solutions using Apache Airflow or Databricks Workflows, ensuring reliable pipeline execution through monitoring, scheduling, and operational controls. Drive data quality, validation, reconciliation, and governance across solutions, adhering to modern Lakehouse architecture principles, data observability, and platform engineering standards.

Develop business-focused data products by enhancing data quality, discoverability, usability, and documentation. Promote responsible AI-assisted engineering to boost productivity, testing, and quality. Review pipeline designs for adherence to engineering, scalability, and performance standards, and troubleshoot complex issues through root cause analysis. Mentor team members on key technologies and best practices, collaborating with stakeholders for end-to-end platform delivery.

A seasoned Senior Data Engineer with 7-8 years of experience in designing and implementing scalable data engineering solutions. Proficiency in mandatory skills including Amazon Kinesis, Apache Spark, Data Quality & Validation, PySpark, SQL, Apache Airflow, and Delta Lake on Databricks is essential.

Experience with additional skills such as Python and DevOps & CI/CD basics is advantageous. The role requires a strong understanding of modern data architecture principles, including Lakehouse architecture and data observability practices.

Key attributes include demonstrating strong ownership, effective collaboration with stakeholders, a quality-focused engineering mindset, and strong analytical thinking. Adaptability to evolving technologies, effective communication of delivery status and risks, and meticulous attention to detail are crucial for success. Encouraging continuous improvement and supporting knowledge sharing are also vital aspects of this role.