Data Infrastructure Team Lead

Description About Immunai: Immunai is an AI-driven platform company focused on improving drug discovery and development by decoding the human immune system. We combine large-scale single cell immune data, advanced machine learning, and strong engineering to help pharmaceutical and research partners make better, more informed decisions throughout the drug development process. Our long-term goal is to reduce drug development failure rates and help more effective medicines reach patients. We’re building this platform thoughtfully and collaboratively, bringing together expertise across biology, AI, engineering, and business. Immunai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. About the role: We are looking for an experienced Data Infrastructure Team Lead to lead our Data Infrastructure team. In this role, you will have formal people-management responsibility for a multidisciplinary team that builds, productionizes, and improves big data and analytics infrastructure. You will be responsible for team delivery, combining technical depth, agile leadership, and an AI-first mindset to help the team build reliable systems, improve how it works, and continuously raise its engineering standards. Location: Ramat Gan (Bursa area), Israel - Hybrid model What will you do? Lead, manage, and develop a data infrastructure team of approximately 5-7 people. Take responsibility for team delivery, including planning, prioritization, execution, and continuous improvement. Lead agile ways of working, including scrum-based delivery practices. Guide the design, productionization, and ongoing improvement of big data and analytics infrastructure. Partner closely with cross-functional stakeholders across technical, scientific, and business interfaces. Help the team adopt AI-first engineering practices, including automation and AI-assisted development workflows. Drive improvements in team processes, technical quality, delivery predictability, and collaboration. Support architectural and technical decisions across cloud infrastructure, data pipelines, orchestration, and production systems. Create an environment where team members can grow, contribute, and improve together. Requirements Required qualifications: 5+ years of experience leading data engineering, data infrastructure, or related engineering teams, of 5+ people - a must Strong technical background in data infrastructure, cloud computing, and production systems - a must Hands-on experience with data warehousing tools such as Snowflake or BigQuery, and data pipeline orchestration tools such as Dagster or Apache Airflow - a must Experience developing in Python Experience productionizing big data, analytics, or data platform infrastructure Experience leading agile teams and working with scrum methodologies Demonstrated ability to lead multidisciplinary teams and collaborate effectively across interfaces. Track record of helping teams improve how they work, including process improvement, automation, technical quality, or delivery practices. Strategic mindset with the ability to connect technical decisions to team and business goals. Strong English communication skills, written and spoken, for working with an international and multilingual team. Preferred qualifications: Experience working in a scientific domain. Experience in biotech, immunology, life sciences, or another biology-adjacent domain. Experience with agentic AI concepts, tools, or development patterns. Experience helping software or data engineering teams transition toward AI-first development processes. Familiarity with modern automation practices across engineering workflows, infrastructure, and delivery pipelines. Desired personal traits: AI-first and automation-oriented mindset. Strong people's leadership instincts and a genuine drive to help others improve. Collaborative, clear, and thoughtful communication style. High ownership, good energy, and comfort operating across disciplines. Curious, pragmatic, and able to balance strategy with hands-on technical judgment.