Join Iris Software, a rapidly expanding IT services company recognized among India's Top 25 Best Workplaces in the IT industry. We offer an award-winning culture that values your talent and ambitions, providing a launchpad for your career growth. Our vision is to be the most trusted technology partner for our clients and the first choice for top industry professionals seeking to realize their full potential.

We collaborate with enterprise clients across financial services, healthcare, transportation & logistics, and professional services, focusing on complex, mission-critical applications. Our expertise spans Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation, utilizing the latest technologies.

As a Senior Data Engineer, you will design scalable data engineering solutions using PySpark and modern distributed data processing frameworks. Key responsibilities include defining data ingestion, transformation, and processing architectures, and optimizing Snowflake or Delta Lake on Databricks for enterprise-scale data platforms. You will lead the implementation of high-performance batch and streaming data pipelines, and design event-driven architectures using Apache Kafka or Amazon Kinesis.

Further duties involve establishing monitoring, scheduling, and operational controls for reliable pipeline execution. You will drive data quality, validation, reconciliation, and governance practices, adhering to modern Lakehouse architecture principles and data observability. Promoting responsible AI-assisted engineering capabilities and mentoring team members on best practices are also crucial aspects of this role. You will troubleshoot complex issues through root cause analysis and collaborate with stakeholders for end-to-end data platform delivery.

We are seeking a Senior Data Engineer with 7-8 years of experience, proficient in mandatory skills including Amazon Kinesis, Data Quality & Validation, PySpark, Python, SQL, Databricks Workflows, and Delta Lake on Databricks. Familiarity with Snowflake, Apache Kafka, and Apache Airflow is also essential.

Candidates should demonstrate strong ownership, collaborate effectively with teams and stakeholders, and promote quality-focused engineering. Essential behavioral competencies include strong analytical thinking, adaptability to evolving technologies, clear communication regarding delivery status, high attention to detail, and a drive for continuous improvement. Balancing scalability, performance, reliability, and business priorities while promoting innovation through modern data engineering and AI-assisted development approaches is key.