Join Iris Software, recognized as one of India's Top Workplaces in the IT industry, and embark on a journey to do your best work. We are a fast-growing IT services company focused on empowering our clients with technology-enabled transformations.

At Iris, we are dedicated to being our clients' most trusted technology partner and a premier destination for top industry professionals. With a global presence and a team of over 4,300 associates, we excel in delivering complex, mission-critical applications across financial services, healthcare, transportation, and professional services. Our expertise spans Application & Product Engineering, Data & Analytics, Cloud, DevOps, and more, utilizing cutting-edge technologies.

Design and implement scalable data engineering solutions using PySpark and advanced distributed data processing frameworks. Architect data ingestion, transformation, and processing pipelines aligned with business objectives, and optimize Snowflake or Delta Lake on Databricks for enterprise-scale data platforms.

Lead the development of high-performance batch and streaming data pipelines, and design event-driven architectures using technologies like Apache Kafka or Amazon Kinesis. Establish data streaming standards, integration frameworks, and scalable processing patterns.

Architect workflow orchestration solutions with Apache Airflow or Databricks Workflows, ensuring reliable pipeline execution through robust monitoring, scheduling, and operational controls. Drive data quality, validation, reconciliation, and governance practices across all data engineering solutions.

Develop data engineering solutions based on modern Lakehouse architecture principles, data observability, and platform engineering standards to enhance scalability, reliability, and operational visibility. Foster the development of business-focused data products by improving data quality, discoverability, usability, and documentation.

Promote the responsible use of AI-assisted engineering capabilities to boost development productivity, testing, documentation, and overall engineering quality. Review data pipeline designs and implementations to ensure adherence to engineering, scalability, and performance standards. Troubleshoot complex data processing, workflow, and streaming platform issues via detailed root cause analysis.

Mentor team members on key technologies like PySpark, Snowflake, Delta Lake, Kafka, Kinesis, and Airflow, along with data engineering best practices. Collaborate with cross-functional teams and stakeholders to ensure successful end-to-end data platform delivery.

We are seeking an experienced Senior Data Engineer with 7-8 years of hands-on experience in designing and implementing robust data solutions. Essential technical skills include Amazon Kinesis, Apache Spark, Data Quality & Validation, PySpark, SQL, Apache Airflow, and Delta Lake on Databricks.

Additional proficiency in Python and a foundational understanding of DevOps & CI/CD practices are highly desirable. The ideal candidate will demonstrate strong ownership, a quality-focused engineering mindset, and excellent analytical thinking to tackle complex data challenges.

Behavioral competencies such as effective collaboration, adaptability to evolving technologies, clear communication, meticulous attention to detail, and a commitment to continuous improvement are crucial. Balancing scalability, performance, reliability, and business priorities while promoting innovation through modern practices is key to success in this role.