Advance your career at one of India's top IT workplaces! Iris Software, a rapidly growing IT services company, offers an award-winning culture where your ambition and talent are valued. Join us as a Data Engineer - Lead and shape your success story with cutting-edge projects.

Our vision is to be a premier technology partner, empowering clients across financial services, healthcare, transportation, and professional services. We specialize in complex, mission-critical applications utilizing technologies like Data & Analytics, Cloud, DevOps, and AI.

At Iris, your role is a launchpad for growth. We champion the "Build Your Future. Own Your Journey." philosophy, fostering an environment where your contributions have a real impact. Benefit from personalized career development, continuous learning, and mentorship.

Define and implement enterprise data engineering strategy, establishing robust standards, governance frameworks, and best practices. Lead the design of large-scale data processing architectures using PySpark and modern data platforms, including Snowflake and Delta Lake. Drive real-time and event-driven data initiatives with Apache Kafka or Amazon Kinesis, and establish governance for ingestion, transformation, and streaming.

Develop standards for workflow orchestration, scheduling, and operational governance with tools like Apache Airflow or Databricks Workflows. Create frameworks for data quality, validation, monitoring, and operational excellence. Define enterprise standards for data products, metadata management, discoverability, and trusted business data consumption.

Establish architecture standards for modern Lakehouse platforms, data observability, and scalable cloud-native data ecosystems to support enterprise analytics and AI. Define AI-ready data foundations for structured and unstructured data, vector architectures, and future GenAI initiatives. Collaborate with business stakeholders to translate objectives into scalable data platform capabilities and architecture decisions.

Drive the adoption of AI-assisted engineering practices to enhance developer productivity, code quality, and delivery. Lead architecture reviews, guiding teams on distributed processing, streaming architectures, and engineering best practices. Identify and mitigate platform risks, scalability bottlenecks, and architectural challenges. Foster a culture of innovation, quality, and continuous improvement.

Proven expertise in PySpark, Delta Lake on Databricks, Databricks Workflows, and Apache Kafka is essential.

Experience with Databricks is highly desirable.

Demonstrate leadership and accountability in driving data engineering excellence across programs and initiatives.

Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.

Promote a culture of innovation, quality, continuous improvement, and data engineering excellence.