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

Our vision is to empower clients through technology-enabled transformations across diverse sectors including financial services, healthcare, and transportation. We specialize in complex, mission-critical applications leveraging cutting-edge technologies such as Cloud, DevOps, Data & Analytics, and AI.

At Iris, we believe in "Build Your Future. Own Your Journey." Every role is an opportunity for growth, offering ownership of your career path, personalized development, continuous learning, and mentorship. Experience a culture that values your potential, amplifies your voice, and ensures your work makes a real impact.

As a Senior Engineer, you will design and implement scalable data engineering solutions using PySpark and distributed frameworks. Key responsibilities include defining data ingestion and transformation architectures, optimizing Snowflake/Delta Lake on Databricks, and leading the development of high-performance batch and streaming data pipelines.

You will architect event-driven data architectures with Kafka or Kinesis, establish data streaming standards, and orchestrate workflows using Airflow or Databricks Workflows. Ensuring robust monitoring, scheduling, and operational controls for reliable pipeline execution is critical.

Driving data quality, validation, and governance practices, alongside designing solutions based on Lakehouse principles and data observability, will be central to your role. You will also promote the responsible use of AI for engineering productivity and mentor team members on best practices.

This role requires a strong proficiency in Databricks Workflows, Apache Spark, Data Quality & Validation, PySpark, SQL, Apache Kafka, and Snowflake. Experience with Delta Lake, Amazon Kinesis, and Apache Airflow is essential.

Candidates must demonstrate strong ownership, effective collaboration with stakeholders, and a commitment to quality-focused engineering. Excellent analytical thinking to tackle complex data engineering challenges and adaptability to evolving technologies are key.

Effective communication regarding delivery status, risks, and improvement opportunities is also a crucial aspect of this position.