At a glance
- Experience
- 3-5 years experience
- Location
- Herzliya
About the role
- The PR&D Data Analyst serves as the technical backbone of the PR&D Analytics & FinOps team by building the data infrastructure and automation that run on AI.
- This role owns the engineering side of data pipelines end-to-end to ensure data is reliable, well-structured, and ready for human and AI consumption.
Responsibilities
- Build data processes and structures that make the team's data usable and accessible for AI/LLM consumption.
- Own the engineering and infrastructure of all data pipelines across the team's domains: build, run, and maintain them so they're reliable, well-structured, and performant.
- Design and build automations that convert existing team processes across both, FinOps and product/R&D analytics, into AI-based workflows.
- Serve as the technical connector between the FinOps and product/R&D analytics tracks, supporting both with shared, well-engineered data infrastructure.
- Troubleshoot and optimize existing pipelines and automations, proactively identifying points of failure or inefficiency.
- Collaborate closely with analysts on both tracks to understand data needs and translate them into robust solutions.
Requirements
- 3-5 years of experience in data engineering, analytics engineering, or a similar hands-on technical data role
- Excellent SQL skills
- Strong hands-on experience building and maintaining data pipelines (e.g., Airflow, dbt, or similar orchestration/transformation tools)
- Experience with BI/visualization tools (Looker, Tableau, or similar)
- Hands-on experience working with AI tools (e.g., Claude Code, Cursor, Codex, or similar) as part of a daily workflow, and building AI-consumable data structures
- Degree in Industrial Engineering, or a similar field
- Independent, able to juggle multiple technical priorities across domains simultaneously
- All-in approach with high accountability, a sense of ownership, and passion
- Good communication skills - able to work cross-functionally with both FinOps and product/R&D analysts
Preferred qualifications
- Hands on experience with automation tools (such as Make)
- Proficiency in Python/ other programmatic languages for automation and data processing
- Experience with building AI agent-based workflows
Skills
- SQL
- Airflow
- Dbt
- Looker
- Tableau
- Claude Code
- Cursor
- Codex
- Make
- Python
