Join 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 client success and professional growth. Our vision is to be a trusted technology partner, empowering enterprise clients across financial services, healthcare, transportation, and professional services with cutting-edge solutions. We specialize in application engineering, data analytics, cloud, DevOps, and quality engineering.

At Iris, we believe in "Build Your Future. Own Your Journey." Your role is a launchpad for career advancement, offering ownership and opportunities to shape your path. Experience a culture that values your potential, amplifies your voice, and fosters impactful work. Benefit from advanced projects, tailored development, continuous learning, and mentorship to achieve your personal and professional best.

As a Senior Engineer specializing in Manual Data Testing, you will design and implement scalable data engineering solutions using PySpark and modern distributed processing frameworks. Key responsibilities include defining 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, architecting event-driven data solutions with Apache Kafka or Amazon Kinesis, and establishing data streaming standards. Designing workflow orchestration with Apache Airflow or Databricks Workflows, alongside implementing robust monitoring, scheduling, and operational controls, is crucial.

Drive data quality, validation, reconciliation, and governance. Design solutions adhering to Lakehouse architecture, data observability, and platform engineering standards to enhance scalability and reliability. Develop business-focused data products by improving data quality, discoverability, and usability. Promote AI-assisted engineering to boost productivity and quality. Review pipeline designs for adherence to standards and troubleshoot complex platform issues. Mentor team members on best practices and collaborate with stakeholders for end-to-end platform delivery.

This role requires a strong foundation in data engineering principles and hands-on experience with key technologies. Essential skills include Databricks Workflows, PySpark, Apache Kafka, and Delta Lake on Databricks. Experience with Snowflake, Apache Airflow, and Amazon Kinesis is also vital for designing and implementing robust data solutions.

Successful candidates will demonstrate strong ownership, collaborate effectively with diverse teams and stakeholders, and promote quality-focused engineering through proactive validation and continuous improvement. A strong analytical mindset is necessary to tackle complex data engineering and platform challenges.

We are looking for individuals who can drive data engineering excellence, contribute to a culture of innovation, and help shape the future of our data platforms.