Data Lead

Sedric is the AI compliance platform for financial services. Our agentic AI automates oversight across customer communications and marketing — real-time guardrails on calls, pre-approval review on every marketing asset, and post-publication monitoring across partners and channels. Because every customer gets a dedicated compliance model, accuracy goes up, false positives go down, and compliance teams move faster without taking on more risk.
Our data lives in silos — Postgres, Snowflake, product usage, customer interactions, billing, infrastructure costs. Each one tells part of the story. Nobody sees the whole picture. When leadership asks “why did our cloud costs jump 30%?” or “which customers are actually driving growth?”, we spend days stitching together an answer that should take an hour. That gap slows down the decisions that matter most.
We’re hiring our first Data Lead to close it. This is an engineer who thinks like an operator: you’ll build the foundation — architecture, pipelines, models — and then use it to connect product behavior, customer outcomes and business performance into one view that drives strategy. No inherited warehouse, no dashboard graveyard, no analyst backlog. A blank page, and a company that needs you to fill it.
The Data Lead reports to our VP Product, is based in Tel Aviv (hybrid), and works daily with Product, Engineering, Operations (GTM) and Finance.

Requirements:

You’re a fit if…
You bring a data engineer’s foundation, an analyst’s business instinct, and the judgment to know when a simple query beats a sophisticated pipeline.

You have 5+ years of hands-on experience in data engineering or analytics engineering, with real exposure to business analytics
Your SQL is strong — this is a must — and you have deep experience with relational databases
You’ve built production-grade ETL pipelines and data models, not one-off scripts — ideally on Snowflake and dbt, with Python where it’s needed
You’ve built BI dashboards (Looker, Tableau, Omni or similar) and defined KPIs people actually trust
You connect the dots across domains — product usage to revenue, customer behavior to cost — and turn to an ambiguous question like “why did costs go up?” into a validated answer and a recommendation
You explain complex analysis clearly to engineers and executives, and you use it to move decisions, not just inform them
You’re skeptical of numbers that don’t add up, and won’t ship a dashboard you don’t trust yourself
You’ve been the first or only data person somewhere before, or you’re ready to be
Bonus: experience with product analytics or customer interaction data (calls, chats, messages), early-stage high-growth startups, or AI-driven products

You’re probably not a fit if…

You want a fully built warehouse and a backlog of tickets waiting for you
You’d rather build the perfect pipeline than answer the business question in front of you
You’re a pure analyst who hands off the moment data needs engineering — or a pure engineer who stops at the pipeline
You need someone else to tell you which metrics matter
You see BI as a reporting function, not a decision-making one
You’re uncomfortable being the only data person in the room

What You’ll Own:

The full data lifecycle — from raw data and modeling to insight, recommendation and measurable impact. You build the infrastructure, but the job is the decisions it enables. You go from data to analysis to action, and you own the outcome.

Data architecture — design and evolve the stack connecting Postgres, Snowflake, product usage, customer interaction, billing and infrastructure cost data into one source of truth
Pipelines and models — build and maintain production-grade ETL pipelines and clean, reusable data models in Snowflake and dbt
Data quality — set up checks and monitoring so nobody has to wonder whether a number is right
Product KPIs — define the metrics that matter across product, operations and the business, with one shared definition per metric
Strategic analysis — connect product usage, customer behavior, revenue and costs to answer the questions leadership is actually asking: where we grow, where we leak margin, what to build next
Product enablement — give Product the usage, adoption and retention insight it needs to prioritize, and make our own AI measurable: model accuracy, false positives, customer outcomes
Business operations enablement — give GTM and Finance self-serve dashboards, alerting and analysis on pipeline, revenue, unit economics and cloud costs
Impact — turn findings into initiatives with Product, Operations, Engineering and Finance, then measure whether they worked
AI-driven execution — use AI tools to write, review and debug SQL and dbt faster, so your time goes into judgment, not boilerplate

What Success Looks Like:

Day 30 — Mapped every data source across product, customer interactions, billing and infrastructure, and shipped a data quality audit flagging the biggest gaps and inconsistencies.
Day 60 — Shipped the core data models and the first set of high-value product and business dashboards, with KPI definitions every team has signed off on.
Day 90 — Delivered a data roadmap, moved at least one core metric from disputed to trusted, and brought leadership a data-driven recommendation that changed a decision.

Compensation & Benefits:

Competitive salary aligned with industry standards
Meaningful equity in a high-growth compliance AI company
Direct access to leadership — no bureaucracy between you and impact
Flexible, output-first culture — we care what you ship, not when you log on
Work on problems that matter in an industry that’s ready to change