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Sedric is an AI compliance platform for financial services that uses agentic AI to automate oversight across customer communications and marketing. The platform provides real-time guardrails on calls, pre-approval review on marketing assets, and post-publication monitoring across partners and channels. Each customer receives a dedicated compliance model, improving accuracy, reducing false positives, and enabling compliance teams to work faster without increasing risk.
The company's data currently lives in silos across multiple systems: Postgres, Snowflake, product usage data, customer interactions, billing, and infrastructure costs. Each source tells part of the story, but no one sees the complete picture. When leadership asks critical questions like "why did cloud costs jump 30%?" or "which customers are actually driving growth?", the organization spends days stitching together answers that should take hours. This gap slows down strategic decision-making.
Sedric is hiring its first Data Lead to close this gap. This role is for an engineer who thinks like an operator. You will build the foundation—architecture, pipelines, and models—and then use it to connect product behavior, customer outcomes, and business performance into a unified view that drives strategy. This is a blank-page opportunity with no inherited warehouse, no dashboard graveyard, and no analyst backlog to inherit.
The Data Lead reports to the VP Product, is based in Tel Aviv (hybrid), and works daily with Product, Engineering, Operations (GTM), and Finance teams. This is a founding data role at a growth-stage AI company in a high-compliance vertical.
REQUIREMENTS:
The posting does not explicitly state years of experience, required skills, education, or certifications. However, the role implicitly requires: strong data engineering and architecture skills; experience building data pipelines and warehouse infrastructure; proficiency with SQL, cloud data platforms (Snowflake, Postgres), and analytics; ability to translate business questions into data models; and experience working cross-functionally with product, engineering, and finance teams.
