Join Iris Software, recognized as one of India's Top 25 Best Workplaces in the IT industry. We are a rapidly growing IT services company dedicated to being a trusted technology partner and a premier destination for industry professionals.

Our vision is to empower clients through technology-enabled transformation across diverse sectors like financial services, healthcare, and transportation. We tackle complex, mission-critical applications using cutting-edge technologies including advanced application engineering, data and analytics, cloud solutions, and MLOps.

At Iris, your career is a launchpad for growth. We believe in empowering our associates with "Build Your Future. Own Your Journey." We foster an environment where your potential is recognized, your contributions are valued, and your work makes a tangible impact. Through challenging projects, personalized development, continuous learning, and mentorship, we support your holistic professional and personal advancement.

Architect and implement scalable data engineering solutions leveraging PySpark and modern distributed data processing frameworks. Define robust data ingestion, transformation, and processing architectures aligned with business objectives.

Design and optimize Snowflake or Delta Lake on Databricks solutions for enterprise-scale data platforms. Lead the development of high-performance batch and streaming data pipelines, including event-driven architectures using Apache Kafka or Amazon Kinesis.

Establish comprehensive data streaming standards, integration frameworks, and scalable processing patterns. Architect workflow orchestration solutions using Apache Airflow or Databricks Workflows, ensuring reliable pipeline execution through robust monitoring, scheduling, and operational controls.

Drive data quality, validation, reconciliation, and governance across all data engineering solutions. Implement modern Lakehouse architecture principles, data observability practices, and platform engineering standards to enhance scalability, reliability, and operational visibility.

Develop business-focused data products by improving data quality, discoverability, usability, and documentation. Promote responsible AI-assisted engineering to boost development productivity and quality. Review and ensure adherence to engineering, scalability, and performance standards for all data pipeline designs and implementations.

Troubleshoot complex data processing, workflow, and streaming platform issues with detailed root cause analysis. Mentor team members on PySpark, Snowflake, Delta Lake, Kafka, Kinesis, Airflow, and best practices in data engineering. Collaborate effectively with cross-functional teams to ensure end-to-end data platform delivery.

A seasoned professional with 7-8 years of experience in Data Engineering is required. Proficiency in mandatory skills including Amazon Kinesis, Apache Spark, Data Quality & Validation, PySpark, SQL, Apache Airflow, and Delta Lake on Databricks is essential.

Additional expertise in Python and a foundational understanding of DevOps & CI/CD practices are beneficial. This role demands strong ownership, collaborative spirit, and a quality-focused engineering mindset. Candidates should possess excellent analytical thinking, adaptability to evolving technologies, and effective communication skills.

A high degree of attention to detail in data architecture, pipeline design, testing, and implementation is crucial. Commitment to continuous improvement, knowledge sharing, and mentoring is expected. The ability to balance scalability, performance, reliability, and business priorities is key to success. Innovation through modern data engineering practices and AI-assisted development is encouraged.