Join Iris Software, a distinguished IT services company recognized among India's Top 25 Best Workplaces. We are seeking a Lead Data Engineer to spearhead our data engineering initiatives and contribute to an award-winning culture that champions talent and ambition. Our vision is to be a premier technology partner for enterprises, empowering them through data-driven transformation across various sectors.

At Iris, your career is a journey of growth and ownership. We foster an environment where your potential is recognized, your voice is heard, and your contributions make a significant impact. Through cutting-edge projects, dedicated career development, continuous learning, and mentorship, we are committed to your personal and professional evolution.

Lead the definition and execution of enterprise data engineering strategy, aligning with organizational goals and modernization efforts. Establish robust data engineering standards, governance frameworks, and best practices. Design large-scale data processing architectures using PySpark and modern data platforms, including Snowflake and Delta Lake. Drive real-time data initiatives with technologies like Apache Kafka and Amazon Kinesis. Define standards for data ingestion, transformation, streaming, and processing frameworks.

Oversee workflow orchestration and operational governance with tools like Apache Airflow or Databricks Workflows. Implement data quality, validation, monitoring, and operational excellence frameworks. Establish standards for data products, metadata management, and trusted business data consumption. Define architecture for Lakehouse platforms, data observability, and scalable cloud-native ecosystems.

Develop strategies for AI-ready data foundations, supporting structured and unstructured data processing, vector architectures, and future GenAI applications. Collaborate with stakeholders to translate business needs into scalable data capabilities. Promote AI-assisted engineering practices to enhance developer productivity and delivery effectiveness. Conduct architecture reviews to ensure solutions meet performance and scalability objectives. Guide teams on distributed processing and streaming architectures. Identify and mitigate platform risks and architectural challenges. Drive continuous improvement initiatives for platform maturity and engineering excellence.

This leadership role requires a strong command of mandatory skills including Amazon Kinesis, PySpark, Apache Kafka, Databricks Workflows, and Delta Lake on Databricks. Demonstrated leadership and accountability in driving data engineering excellence are essential.

Effective collaboration with diverse teams and business stakeholders is crucial for successful project delivery. The ideal candidate will possess the ability to guide teams on distributed data processing, streaming architectures, modern data platforms, and best engineering practices. Identifying and addressing platform risks, scalability bottlenecks, and architectural challenges will be key responsibilities.