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Requirements:
5+ years of hands-on paid acquisition experience in B2B SaaS, with deep operational expertise across Google, LinkedIn, and Meta. You've personally built, scaled, and optimized campaigns, with a strong understanding of performance drivers across the funnel.
Proven experience building AI-powered paid media capabilities, including automation, intelligent bidding, audience modeling, dynamic creative optimization, or similar systems that extend beyond platform-native automation.
A builder's mindset. You're energized by ambiguity, prefer creating processes over inheriting them, and instinctively turn one-off wins into repeatable systems.
Experience owning budget allocation, forecasting, and ROI management across multi-channel, multi-market investments.
Strong expertise in attribution, analytics, and reporting, with the ability to translate complex data into clear, defensible recommendations.
A track record of driving pipeline growth through paid search and paid social, with a relentless focus on experimentation and optimization.
Excellent project management skills and the ability to manage multiple initiatives in a fast-moving environment.

Requirements:
Bachelor's degree in Business Administration, Management, Organizational Psychology, or a related field.
7+ years of relevant experience in strategy, operations, consulting, or business leadership roles, with at least 2-3 years of experience serving as a Chief of Staff to a CEO, founder, or executive leadership team.
Exceptional interpersonal skills and emotional intelligence, with the ability to build trust and influence at all levels of the organization.
Demonstrated ability to influence without formal authority and drive alignment among senior stakeholders with different perspectives.
Outstanding communication, facilitation, and relationship-building skills.
Strong structured thinking and the ability to bring clarity to complexity.
Proven ability to turn discussions into actionable plans and ensure disciplined execution.
Highly organized, detail-oriented, and able to manage multiple priorities simultaneously.
Strong business judgment, problem-solving capabilities, and organizational awareness.
Comfortable operating in ambiguity and helping others navigate change.
High level of integrity, discretion, and professionalism.
Native-level English proficiency.

Requirements:
Proven experience designing, building, and operating large-scale data infrastructure in production environments.
Strong engineering fundamentals in databases, distributed systems, concurrency, storage, and reliability.
Strong knowledge of analytical engines, columnar databases, and data warehouse/lakehouse architectures.
Experience with data orchestration tools and distributed storage solutions.
Understanding of data security, privacy, governance, lineage, auditability, and access control.
Ability to make pragmatic architecture decisions, balance maitenance-vs-delivery tradeoffs, and optimize for scalability, reliability, simplicity, and cost.
Proven ability to lead a team, mentor engineers, communicate clearly, and work effectively with cross-functional stakeholders.
Experience building infrastructure for data pipelines, orchestration, streaming, analytical workloads, and large-scale data processing.
Experience with observability, monitoring, alerting, incident management, SLOs, and production operations for data systems.
Strong understanding of AI-ready data platforms, including metadata, semantic layers, retrieval patterns, data quality, governance, and permission-aware access to data.
Expertise with relational databases; PostgreSQL experience is a plus.
Hands-on experience with streaming systems such as Apache Kafka or equivalent technologies.
Bonus Skills
Experience with cloud-based data infrastructure solutions, such as AWS or GCP.
Familiarity with Kubernetes.
Experience with modern lakehouse technologies, open table formats, metadata catalogs, or data governance platforms.
Experience supporting AI, ML, LLM, semantic search, or agentic product use cases on top of production data.
Experience building multi-tenant SaaS infrastructure, especially with sensitive or permission-heavy data.
Experience with cost optimization and FinOps practices for data platforms.
Experience creating internal developer platforms, self-service data tooling, or paved-road infrastructure for engineering teams.

Requirements:
6+ years of product management experience in B2B SaaS or platform-oriented environments
Experience working on AI-powered products, preferably including GenAI
Good technical understanding and ability to work closely with engineers on complex systems
Familiarity with concepts such as LLMs, retrieval, or data-driven systems (hands-on experience is a plus)
Strong product thinking, ability to balance user needs, business goals, and technical constraints
Experience working with data, metrics, and experimentation
Ability to work across teams and drive alignment in ambiguous environments
Strong communication skills and ability to clearly articulate product decision
Strong advantage
Experience building platform products, APIs, or internal tools
Familiarity with AI evaluation, monitoring, or experimentation frameworks
Background in technical roles (engineering, data, etc.)
Experience in domains requiring high trust and reliability (e.g., HR, finance, security)

Requirements:
7+ years of experience in software engineering with a strong backend or full-stack focus.
3+ years of experience as an engineering team lead in a fast-paced environment.
Strong hands-on experience with modern backend technologies and system design.
Proven experience building and maintaining scalable distributed systems or platform capabilities.
Experience working with cloud platforms (AWS or equivalent).
Solid understanding of modern frontend technologies (e.g., React, component-based architecture).
Ability to balance technical depth, people leadership, and product/platform thinking.
Excellent communication skills in English (Hebrew is a plus).
Passion for both people management and hands-on engineering.
Nice to Have:
Experience building or operating AI / LLM-based systems in production.
Experience with extensibility platforms, APIs, or developer ecosystems.
Familiarity with workflow engines, orchestration systems, or event-driven architectures.
Background in developer experience (DevX), internal platforms, or shared services.
Experience with dynamic UI systems or design systems.
Experience mentoring engineers or leading internal guilds / tech forums.