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

With a global presence across India, the U.S.A., and Canada, Iris empowers enterprise clients in financial services, healthcare, transportation & logistics, and professional services through technology-enabled transformation. Our expertise spans mission-critical applications utilizing cutting-edge technologies in Application & Product Engineering, Data & Analytics, Cloud, DevOps, Data & MLOps, Quality Engineering, and Business Automation.

Architect and implement robust, scalable data engineering solutions leveraging PySpark and modern distributed data processing frameworks.

Design comprehensive data ingestion, transformation, and processing architectures tailored to business and analytical needs.

Develop and optimize enterprise-scale data platforms using Snowflake or Delta Lake on Databricks.

Lead the creation of high-performance batch and streaming data pipelines.

Design and refine event-driven data architectures with Apache Kafka or Amazon Kinesis.

Establish robust standards for data streaming, integration frameworks, and scalable processing patterns.

Architect workflow orchestration solutions using Apache Airflow or Databricks Workflows.

Implement comprehensive monitoring, scheduling, and operational controls for reliable pipeline execution.

Champion data quality, validation, reconciliation, and governance across all data engineering initiatives.

Design data engineering solutions based on Lakehouse architecture principles, data observability, and platform engineering standards to enhance scalability, reliability, and operational visibility.

Drive the development of impactful business data products by improving data quality, discoverability, usability, documentation, and trusted consumption.

Promote the responsible application of AI-assisted engineering 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 benchmarks.

Resolve 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 best practices in data engineering.

Mandatory skills include proficiency in Databricks Workflows, PySpark, Apache Kafka, and Snowflake.

Demonstrate strong ownership and a commitment to data engineering excellence.

Effectively collaborate with diverse teams and business stakeholders to ensure seamless project delivery.

Promote a quality-focused engineering approach through proactive validation, optimization, and continuous improvement.

Apply robust analytical thinking to tackle complex data engineering and platform challenges.

Exhibit adaptability in navigating evolving technologies, data ecosystems, and business requirements.

Communicate clearly regarding delivery status, identified risks, dependencies, and opportunities for enhancement.

Maintain meticulous attention to detail across data architecture, pipeline design, testing, and implementation activities.