Advance your career at Iris Software, recognized as one of India's Top 25 Best Workplaces in the IT industry. We are a rapidly expanding IT services company committed to empowering our employees to shape their professional journeys. Our vision is to be the premier technology partner for our clients and a destination for top industry talent.

We specialize in delivering cutting-edge technology solutions across financial services, healthcare, transportation & logistics, and professional services. Our expertise spans complex application engineering, data analytics, cloud solutions, DevOps, MLOps, quality engineering, and business automation.

As a Senior Data Engineer, you will architect and implement scalable data solutions. This includes designing robust data ingestion, transformation, and processing frameworks using PySpark and modern distributed systems.

You will develop high-performance batch and streaming pipelines, and optimize enterprise-scale data platforms leveraging Snowflake or Delta Lake on Databricks. Your role will involve designing event-driven architectures with Apache Kafka or Amazon Kinesis, and establishing standards for scalable processing patterns. You will also architect workflow orchestration solutions using Apache Airflow or Databricks Workflows, ensuring reliable pipeline execution through comprehensive monitoring and operational controls.

Furthermore, you will drive data quality, governance, and observability across our data engineering solutions, adhering to Lakehouse architecture principles. You will focus on developing business-centric data products, enhancing data discoverability, usability, and documentation. Promoting AI-assisted engineering for improved productivity and quality, reviewing designs for adherence to standards, and conducting root cause analysis for complex issues are also key aspects of this role. You will mentor junior team members and collaborate with stakeholders for end-to-end data platform delivery.

We are seeking skilled professionals with mandatory expertise in PySpark, Amazon Kinesis, Delta Lake on Databricks, and Databricks Workflows. Familiarity with Snowflake, Apache Kafka, and Apache Airflow is also essential.

Key attributes include strong ownership, effective collaboration with diverse teams and stakeholders, and a commitment to quality-focused engineering. You should possess robust analytical thinking to tackle complex data engineering challenges and demonstrate adaptability in a dynamic technological landscape. Excellent communication skills are vital for reporting on delivery status, risks, and opportunities.

We value individuals who drive data engineering excellence, promote continuous improvement, and can manage evolving business requirements and data ecosystems. Your ability to mentor others and contribute to a collaborative team environment will be highly regarded.