Join a distinguished IT services company recognized among India's Top 25 Best Workplaces. At Iris Software, we empower you to achieve your career best within an award-winning culture that champions talent and ambition.

Our vision is to be the premier technology partner for our clients and the preferred destination for top industry professionals. With a global presence across India, the U.S.A., and Canada, we drive technology-enabled transformations for enterprise clients in financial services, healthcare, transportation & logistics, and professional services. We specialize in complex, mission-critical applications leveraging cutting-edge technologies like Data & Analytics, Cloud, DevOps, and AI/ML.

Key responsibilities include designing scalable data engineering solutions using PySpark and modern distributed data processing frameworks. You will define data ingestion, transformation, and processing architectures, and optimize Snowflake or Delta Lake on Databricks solutions for enterprise-scale platforms.

Lead the implementation of high-performance batch and streaming data pipelines, architecting event-driven data architectures with technologies like Apache Kafka or Amazon Kinesis. Establish data streaming standards, integration frameworks, and scalable processing patterns.

Architect workflow orchestration solutions using Apache Airflow or Databricks Workflows. Implement robust monitoring, scheduling, and operational controls for reliable pipeline execution. Drive data quality, validation, reconciliation, and governance practices.

Design data engineering solutions aligned with Lakehouse architecture principles, data observability, and platform engineering standards to enhance scalability, reliability, and operational visibility. Develop business-focused data products by improving data quality, discoverability, and usability.

Promote the responsible use of AI-assisted engineering capabilities to boost development productivity and quality. Review data pipeline designs and implementations, ensuring adherence to engineering, scalability, and performance standards. Troubleshoot complex data processing, workflow, and streaming platform issues through detailed root cause analysis. Mentor team members on PySpark, Snowflake, Delta Lake, Kafka, Kinesis, Airflow, and data engineering best practices. Collaborate with various teams and stakeholders to ensure seamless end-to-end data platform delivery.

We are seeking a Senior Engineer with mandatory skills in Databricks Workflows, Delta Lake on Databricks, Snowflake, PySpark, and Amazon Kinesis. The ideal candidate will possess strong capabilities in Big Data (PySpark, Apache Spark), Data Engineering (Data Quality & Validation, ETL Concepts), Python, and Database Programming (SQL, PL/SQL).

Proficiency in cloud technologies, specifically AWS (AWS SNS, AWS SQS, AWS Kinesis), and experience with Databricks are essential. Familiarity with data visualization tools and ETL concepts is also required.

Behavioral competencies include demonstrating strong ownership, effective collaboration with teams and business stakeholders, a commitment to quality-focused engineering, and strong analytical thinking to address complex data engineering challenges. Experience with Snowflake and Big Data technologies is a key requirement for this role.