Join Iris Software, a recipient of "India’s Top 25 Best Workplaces in IT industry" award, and shape your career in a dynamic, growth-oriented environment. We are a rapidly expanding IT services company committed to being a trusted technology partner and a premier destination for top industry talent.

Our vision is to empower enterprise clients across financial services, healthcare, transportation & logistics, and professional services with cutting-edge technology transformations. We specialize in complex, mission-critical applications leveraging the latest advancements in Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation.

At Iris, your role is a springboard for professional development, guided by our "Build Your Future. Own Your Journey." philosophy. We cultivate a culture that values your potential, amplifies your voice, and ensures your work makes a significant impact. Through challenging projects, tailored career advancement, continuous learning, and mentorship, we are dedicated to your personal and professional growth.

As a Senior Engineer, you will design and implement scalable data engineering solutions utilizing PySpark and modern distributed data processing frameworks. Your responsibilities will include defining robust data ingestion, transformation, and processing architectures that align with business and analytical goals.

You will architect and optimize Snowflake or Delta Lake on Databricks solutions, establish high-performance batch and streaming data pipelines, and design event-driven architectures using Apache Kafka or Amazon Kinesis. Defining data streaming standards, integration frameworks, and scalable processing patterns will be key. You will also architect workflow orchestration solutions with Apache Airflow or Databricks Workflows, ensuring reliable pipeline execution through robust monitoring, scheduling, and operational controls.

Furthermore, you will drive data quality, validation, reconciliation, and governance practices. Your designs will adhere to modern Lakehouse architecture principles, data observability, and platform engineering standards to enhance scalability, reliability, and operational visibility. You will champion the development of business-focused data products by improving data quality, discoverability, usability, documentation, and trusted consumption. Promoting the responsible use of AI-assisted engineering capabilities to boost development productivity, testing, documentation, and quality is also part of this role. You will review data pipeline designs, troubleshoot complex issues, and mentor team members on best practices, collaborating with cross-functional teams for end-to-end data platform delivery.

This role requires strong technical expertise in PySpark, Apache Kafka, Databricks Workflows, and Delta Lake on Databricks. You should be adept at designing and optimizing data solutions with modern distributed data processing frameworks and data warehousing technologies such as Snowflake or Delta Lake.

Candidates are expected to have experience in designing and implementing high-performance batch and streaming data pipelines, alongside event-driven architectures using platforms like Apache Kafka or Amazon Kinesis. Proficiency in architecting workflow orchestration solutions with tools like Apache Airflow or Databricks Workflows is essential. A strong understanding of data quality, validation, reconciliation, governance practices, Lakehouse architecture principles, and data observability is crucial.

We are looking for individuals who demonstrate strong ownership, promote quality-focused engineering, and possess excellent analytical and problem-solving skills. Adaptability to evolving technologies and effective communication regarding delivery status, risks, and opportunities are key behavioral competencies. Mentoring junior team members on best practices and collaborating effectively with stakeholders to ensure successful platform delivery are integral aspects of this position.