דרושים » דאטה » ראש /ת צוות פיתוח והובלה טכנולוגית של מערכות מידע

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.

Requirements:
3+ years of data engineering management leading architecture and strategy
6+ years of experience building large-scale (at least 5 million events a day) distributed systems in Data Engineering, ML Engineering, or Software Engineering roles.
Proven ownership of production-grade data or ML platforms, including delivery and adoption across R&D and Product stakeholders.
Hands-on experience building and operating high-scale distributed data systems (Spark, Storm, Flink) in production.
Strong experience with Java and Python in AWS cloud environments.
Advantages:
Proven track record leading multi-disciplinary teams and driving measurable business impact through data/ML systems.
Experience building ML platforms, feature stores, or self-serve data infrastructure at scale.
Deep experience with modern ML/infra stack (PyTorch, TensorFlow, SageMaker, Kubernetes, Argo).
Experience with modern data lakehouse and analytics stack (Iceberg, Athena, ClickHouse, data catalogs, data quality frameworks).
Experience deploying LLM-based systems or AI-driven infrastructure in production environments.

Requirements:
Bachelors or Masters degree in Computer Science, Mathematics, Physics, Engineering, or a related field.
2+ years of experience leading, mentoring, and growing engineering teams.
5+ years of hands-on software/backend engineering experience building large-scale production systems with python.
Strong foundation in algorithms, data structures, and algorithmic complexity.
Some mathematical background preferred, will need the ability to work with 3D geometry, linear algebra, and coordinate transformations.
Experience with Linux, containerization, CI/CD, databases, and modern development workflows.
Experience building scalable data infrastructure, distributed systems, or cloud-based architectures.