Join Iris Software, recognized as one of India's Top 25 Best Workplaces in IT. We are a rapidly expanding IT services company dedicated to helping enterprise clients thrive through technology-enabled transformation. Our expertise spans critical applications across financial services, healthcare, transportation & logistics, and professional services, leveraging cutting-edge technologies like Data & Analytics, Cloud, and DevOps.

At Iris, we believe in empowering our employees. Our "Build Your Future. Own Your Journey." philosophy means you'll have ownership of your career path, with ample opportunities for growth. Experience a culture that values your talent, where your voice matters, and your work drives real impact. We offer exciting projects, personalized development, continuous learning, and mentorship to help you achieve both personal and professional success.

Design and implement scalable data engineering solutions using PySpark and distributed data processing frameworks. Define robust data ingestion, transformation, and processing architectures to meet business and analytical needs. Develop and optimize Snowflake or Delta Lake on Databricks solutions for enterprise-scale data platforms. Lead the creation of high-performance batch and streaming data pipelines. Architect event-driven data architectures utilizing Apache Kafka or Amazon Kinesis. Establish comprehensive data streaming standards, integration frameworks, and scalable processing patterns. Design workflow orchestration solutions using Apache Airflow or Databricks Workflows. Implement monitoring, scheduling, and operational controls to ensure reliable pipeline execution. Drive data quality, validation, reconciliation, and governance across all data engineering solutions. Architect data engineering solutions based on modern Lakehouse principles, data observability, and platform engineering standards. Develop business-focused data products by enhancing data quality, discoverability, usability, documentation, and trusted consumption. Utilize AI-assisted engineering capabilities to boost development productivity, testing, documentation, and overall engineering quality. Conduct thorough reviews of data pipeline designs and implementations to ensure adherence to engineering, scalability, and performance standards. 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 data engineering best practices. Collaborate effectively with various teams and stakeholders to support end-to-end data platform delivery.

Possess 7-8 years of experience in data engineering. Proficiency in mandatory skills: Amazon Kinesis, Apache Spark, Data Quality & Validation, PySpark, SQL, Apache Airflow, and Delta Lake on Databricks. Familiarity with additional skills such as Python and DevOps & CI/CD Basics is beneficial. Demonstrate strong ownership and a commitment to data engineering excellence. Exhibit effective collaboration skills with diverse teams and business stakeholders. Promote a quality-focused engineering approach through proactive validation and continuous improvement. Apply strong analytical thinking to address complex data engineering and platform challenges. Adapt effectively to evolving technologies, data ecosystems, and business requirements. Communicate clearly regarding delivery status, risks, dependencies, and improvement opportunities. Maintain a high level of attention to detail in architecture, design, testing, and implementation. Encourage continuous improvement in data engineering practices and platform operations. Support knowledge sharing and mentoring to enhance team capabilities. Balance scalability, performance, reliability, and business priorities effectively. Promote innovation by adopting modern practices, platform engineering principles, and AI-assisted development.