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We are seeking a skilled and motivated Priority ERP Implementer (Financial Modules) to join our team and lead end-to-end implementation processes for Priority ERP systems. This role focuses on implementing and supporting the Financial modules while working closely with internal teams and clients to optimize business processes. The position is fully on-site in Jerusalem. Responsibilities
* Lead end-to-end implementation of Priority ERP Financial modules (e.g., General Ledger, Accounts Payable, Accounts Receivable, Financial Reporting).
* Analyze business requirements, translate needs into system configurations, and design tailored solutions.
* Configure modules, define workflows, create reports, and customize system functionalities.
* Provide user training, onboarding, and ongoing support for operational teams.
* Perform testing, troubleshooting, and quality assurance for new implementations and system upgrades.
* Collaborate closely with project managers, developers, and cross-functional teams to ensure seamless system integration.
* Document all processes, configurations, and system adjustments.

As the Merchant-Ops Squad Lead, you will be responsible for the team's technical leadership, domain design, and day-to-day execution. You will collaborate closely with product managers and business stakeholders to define priorities and deliver impactful solutions that align with key business goals. We are looking for someone who can drive end-to-end ownership, balance business needs with strong engineering practices, and help shape the direction of our products.

Responsibilities:

Among your key responsibilities, you will lead a highly skilled team that architects and builds scalable systems that serve our customers and partners.

Lead the technical architecture and system design for merchant-facing products
Guide a multidisciplinary squad of frontend, backend and data engineers through implementation, delivery, and iteration
Work closely with product managers and business stakeholders to translate requirements into scalable technical solutions
Ensure development aligns with business objectives, long-term system health, and engineering standards
Conduct and lead design reviews, code reviews, and performance optimization efforts
Drive a culture of technical excellence, ownership, and continuous improvement
Oversee system reliability, monitoring, and incident response within your domain

We are looking for a Knowledge & AI Enablement Manager with proven experience in Knowledge Management and AI driven systems to design, build, and own the complete Knowledge Lifecycle for Technical Support.
This role defines how operational knowledge is created, governed, maintained, and embedded into daily Support workflows, while ensuring it is AI ready and serves as a trusted, high quality input for AI models across the organization.
Key Responsibilities:
Knowledge Lifecycle Ownership:
Own and build the end-to-end Knowledge Lifecycle for Support, from case-driven knowledge creation through continuous improvement and reuse
Define how knowledge is captured from cases, incidents, and escalations and transformed into structured, reusable, high-impact assets
Establish governance models for knowledge quality, validation, ownership, versioning, and ongoing maintenance
Design and operationalize workflows that embed knowledge creation, review, and updates into daily Support activitie
Define and implement a RACI model for knowledge creation, review, approval, and maintenance
Knowledge and AI Readiness:
Own KM platform configuration, Search and analytics tooling, taxonomy, publishing workflows, and integration with case management and AI retrieval pipelines
Define and enforce standards that ensure Support knowledge is structured, tagged, and maintained to be fully consumable by AI systems such as LLMs, RAG pipelines, semantic search, automation, and recommendation engines
Partner closely with Data, AI, and Engineering teams to align knowledge models, structure, and lifecycle with AI consumption requirements and future AI roadmaps
Act as the primary owner of the Support knowledge layer that feeds AI, ensuring consistency, reliability, and scalability
Measurement, Feedback, and Adoption
Define, track, and continuously improve KPIs including knowledge coverage, freshness, reuse, case deflection, and measurable AI performance impact
Establish feedback loops that surface insights from AI usage and knowledge gaps back to Support Engineers, driving better case handling and higher quality knowledge creation
Lead change management initiatives to drive adoption, accountability, and consistent knowledge behaviors across global Support teams.