Join Iris Software, a top-ranked IT workplace, and build your best career. We are a fast-growing IT services company with a vision to be a trusted technology partner. With over 4,300 associates globally, we empower enterprise clients across various sectors with advanced solutions in data, cloud, and application engineering.

At Iris, we believe in "Build Your Future. Own Your Journey." This means providing opportunities for ownership, continuous learning, and mentorship. You’ll work on cutting-edge projects, shape your career path, and make a real impact in a culture that values your potential and your voice.

Define and implement enterprise data engineering strategies and standards, aligning with organizational goals and data modernization efforts. Lead the design of scalable data processing architectures using PySpark and modern data platforms like Snowflake and Databricks. Drive real-time data initiatives with Apache Kafka and establish robust governance frameworks for data ingestion, transformation, and quality.

Develop standards for data products, metadata management, and Lakehouse platforms, ensuring data observability and scalability. Strategize AI-ready data foundations and explore vector-enabled architectures for GenAI initiatives. Collaborate with business stakeholders to translate objectives into data platform capabilities and drive adoption of AI-assisted engineering practices.

8-10 years of experience in data engineering. Proficient in Python, PySpark, SQL, Apache Kafka, Snowflake, and Databricks Workflows. Demonstrated leadership in defining enterprise data engineering strategy, standards, and best practices.

Experience in designing large-scale data processing architectures, real-time data streams, and Lakehouse platforms. Strong understanding of data governance, quality frameworks, and AI-ready data foundation strategies. Proven ability to collaborate with business stakeholders, lead architecture reviews, and drive continuous improvement initiatives.