Join Iris Software, a top-tier IT services company and an award-winning workplace, to build the best work of your career. We are seeking a Senior Data Engineer to contribute to innovative, mission-critical applications leveraging cutting-edge technologies. You'll be part of a rapidly growing organization that values talent, ambition, and ownership of your professional journey.

At Iris, we partner with enterprise clients across financial services, healthcare, transportation & logistics, and professional services, driving their technology-enabled transformations. Our expertise spans Application & Product Engineering, Data & Analytics, Cloud, DevOps, and more, offering a launchpad for your growth and a platform to make a real impact.

Develop and implement scalable data engineering solutions using PySpark and distributed frameworks. Design robust data ingestion, transformation, and processing architectures. Optimize Snowflake or Delta Lake on Databricks for enterprise-scale data platforms. Lead the creation of high-performance batch and streaming data pipelines. Architect event-driven data flows with Apache Kafka or Amazon Kinesis, defining streaming standards and patterns. Build workflow orchestration using Apache Airflow or Databricks Workflows, ensuring reliable pipeline execution through monitoring and scheduling.

Drive data quality, validation, and governance across all solutions. Implement modern Lakehouse architecture principles, data observability, and platform engineering standards for enhanced scalability and reliability. Develop business-focused data products by improving data quality, discoverability, and usability. Mentor team members on data engineering best practices and collaborate with stakeholders for end-to-end platform delivery.

Mandatory skills include proficiency in Databricks Workflows, PySpark, Apache Kafka, and Snowflake. Experience in designing scalable data engineering solutions and defining data ingestion/transformation architectures is essential. You should be adept at optimizing data platforms using Snowflake or Delta Lake, and implementing high-performance batch and streaming pipelines. A strong understanding of event-driven architectures with Apache Kafka or Amazon Kinesis, and workflow orchestration with Apache Airflow or Databricks Workflows is required.

Candidates should possess strong analytical thinking, adaptability, and effective communication skills. Demonstrating ownership, promoting quality-focused engineering, and a collaborative approach are key behavioral competencies. Experience in troubleshooting complex data processing issues and mentoring junior team members is also expected. We seek professionals who can drive data quality and governance, and promote the responsible use of AI-assisted engineering capabilities.