Join Iris Software, recognized as one of India's Top 25 Best Workplaces in the IT industry, and embark on a career-defining journey. As a rapidly growing IT services company, we empower our associates to shape their own success stories. Our vision is to be the most trusted technology partner for our clients and a premier destination for top IT professionals to achieve their full potential.

We serve enterprise clients across financial services, healthcare, transportation & logistics, and professional services, delivering complex, mission-critical applications with cutting-edge technologies. Our expertise spans Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation.

As an AWS Data Engineer with 8-10 years of experience, you will define and lead enterprise data engineering strategy, establishing robust standards, governance frameworks, and best practices. You will design scalable data processing architectures using PySpark and modern data platforms like Snowflake and Databricks, focusing on real-time data initiatives with Apache Kafka.

Key responsibilities include establishing governance for data ingestion, transformation, and streaming; defining workflow orchestration using tools like Apache Airflow or Databricks Workflows; and implementing data quality, monitoring, and operational excellence frameworks. You will also define standards for data products, metadata management, and Lakehouse platforms, preparing the foundation for AI and GenAI initiatives. Driving adoption of AI-assisted engineering practices and leading architecture reviews to ensure scalability and reliability are central to this role. You will collaborate with stakeholders, identify platform risks, and drive continuous improvement in engineering excellence.

We are seeking an experienced AWS Data Engineer with 8-10 years of hands-on experience. Essential technical skills include Python, PySpark, SQL, Apache Kafka, Snowflake, and Databricks Workflows.

This role demands strong leadership, accountability, and strategic thinking to drive data engineering excellence. Effective collaboration with diverse teams and business stakeholders is crucial. A proactive approach to engineering governance, platform standards, and solution excellence, coupled with a commitment to innovation and continuous learning, is highly valued. Candidates should demonstrate strong decision-making skills, balancing performance, scalability, and delivery objectives, while communicating effectively about platform strategy, risks, and outcomes.