ИзраильAI Data Lead
2 нед. назад
ИзраильLeadHybrid
bidata analysisaidata engineering
Lead the data function transition from traditional BI to AI-driven BI in a fast-growing digital therapy platform.
Требования
- 7+ years of experience in data roles, spanning both analytics and infrastructure
- Strong analytical foundation — you understand what makes analysis rigorous, not just what makes it automatable, and can use that to build an AI-driven analytics system that's actually trustworthy
- Hands-on experience integrating AI into data workflows — building it, not just using it; you've shipped an agent or AI analysis flow that people actually use
- Strong SQL skills and experience building scalable data infrastructure
- Track record owning data projects independently with stakeholders across the business
- Solid grounding in experimentation and statistical methods
- Excellent communication and presentation skills at a leadership level
- Comfortable being the most senior (and often only) data voice in the room
Будет плюсом
- Experience with dbt
- Experience with Amplitude
- Experience with BigQuery
- Experience with git-based BI workflows or tools like Modus
- B2C environment experience
Другое
- Data-AI Lead About Us is a fast-growing digital platform reshaping how relationship therapy works. We combine expert-led therapy sessions with AI-powered support to create a blended experience — where clinical depth and intelligent technology weave together into something neither could deliver alone. We call it Continuous Therapy. We've reached product-market fit with strong growth, exceptional engagement, and retention that breaks the category. We're pre-Series A, at the stage where AI moves from feature to foundation — and that shift runs straight through data. Role Overview As Data Lead, you will own how data gets turned into decisions across the company — and lead the shift from traditional BI into AI-driven BI, powered by agents that surface insight in real time. You'll work closely with one data engineer today, with room to grow the team as scope expands. You'll define what the data function looks like when AI is embedded in it, not bolted on — from the underlying infrastructure to the dashboards and agents people actually use. The playbook for AI-native BI is still being written, and you'll write a lot of it. Key Responsibilities Data Strategy & Ownership
- Own the vision and roadmap for the data function, including the shift from classic BI to agent-based, AI-driven BI
- Set priorities in partnership with senior leadership
- Work closely with a data engineer today, with the option to grow the team as the function scales
- AI-Driven BI & Agents
- Drive adoption of AI across data workflows — from automating analyses to agents answering business questions in real time
- Own the semantic and metrics layer so every dashboard and every agent reads the same definitions
- Build in a git-based workflow: dashboards and queries live in git, edited through PRs, built with agents
- Expose our data to agents over MCP so business questions get answered without waiting for a dashboard
- Define where AI speeds things up — and where human judgment still has to hold the line
- Infrastructure & Analytics
- Own the data infrastructure: BigQuery, dbt, pipelines, and data reliability
- Lead complex analytics work, A/B testing, and impact measurement, hands-on
- Define evals and quality metrics for AI features and agents — not just A/B tests on dashboards
- Cross-functional Collaboration
- Partner with product, marketing, finance, operations, and engineering to define analytical needs and turn them into solutions
- Represent the data function to the organization — senior-level storytelling with data
- Data teams are about to look very different — not because dashboards disappear, but because people expect answers faster than a weekly report can deliver. This is a rare opportunity to define what AI-native data leadership looks like at a company sitting on a dataset that doesn't exist anywhere else, with real ownership over how the function grows. If you want to build the data function that a category-defining product deserves, this is it.