But we're not hiring a classic analyst who lives in spreadsheets. Industry analysts spend 60–70% of their time preparing data and only the rest analyzing it. We invert that. You'll use AI agents (Claude Code, Cursor, Codex) to build the workflows, queries, and automations that kill the grunt work — so your time goes to judgment, not janitorial data prep. You write SQL like a native language, and you teach an AI agent to write it for you and check its work.

What You'll Own

• Marketing attribution & measurement: Own how we model attribution and measure performance across channels — first/last/multi-touch, custom windows, cross-channel paths, LTV-driven logic for complex funnels. Make the numbers trustworthy and explainable.
• Cross-channel & funnel analysis: Connect paid media, on-site behavior, CRM, and backend revenue into one end-to-end view. Diagnose where the funnel leaks and why the unit economics do or don't work.
• Paid-channel analytics (Meta & Google): Read these platforms like an operator: how the auction behaves, where spend hides, why CPA spikes, what the data is really telling you.
• AI-driven analytical workflows: Build the workflows, queries, and automations that do the heavy lifting — using AI agents (Claude Code, Cursor, Codex) to generate, refactor, and validate SQL and analysis. Design repeatable pipelines, not one-off pulls.
• Dashboards & insight delivery: Build clear, accessible dashboards and reporting (Power BI / Looker / Tableau-class) that make insight usable by the team and clients.
• Working with the product: Your analytical models and attribution logic feed the AI agent itself. Partner with product, data, and engineering on what "correct" looks like.

Requirements

• Excellent SQL. This is non-negotiable — you write complex queries fluently, reason about data models, and can debug and optimize what an AI agent generates for you.
• Proven experience in marketing analytics and marketing in general — required. You've worked with attribution, cross-channel / end-to-end analytics, and the management and analysis of Meta and Google Ads.
• Hands-on experience with AI coding agents — Claude Code, Cursor, Codex or equivalent — and a track record of building workflows and automations with them.
• You think in systems: repeatable pipelines and automated analysis, not manual one-off pulls.
• Proficiency with version control, particularly Git.
• Strong communication — you translate data into decisions a marketer or founder can act on.
• Professional-level English (our clients are in the US market) and reliable overlap with US business hours.

It'd Be a Plus If

• Python for analysis and data manipulation beyond SQL.
• Experience with dbt, modern data warehouses (BigQuery, Snowflake), or ETL/pipeline work.
• Hands-on with web/measurement tooling — GA4, GTM, server-side tracking, consent.
• Experience in segments with complex funnels and LTV-driven models (EdTech, FinTech, Consumer Apps, B2C SaaS, Healthcare, Home Services).

Red Flags

• "I make dashboards" without understanding the marketing or the attribution underneath.
• SQL only through a BI tool's drag-and-drop — can't write or debug a real query.
• Uses AI agents only as a chat box, never built a workflow or automation with one.
• Marketing-analytics theory with no hands-on Meta / Google Ads experience.

What You Can Expect

• Competitive compensation — aligned with senior marketing-analytics experience.
• AI-first culture — the whole company works in Cursor with premium team access to AI tools.
• Maximum impact — your models shape both client outcomes and the AI product itself.
• Remote — work from anywhere with reliable US-hours overlap.

Hiring Process

1. Application — CV + short answers.
2. Intro call (People & Operations, ~30 min).
3. Technical interview — deep dive on SQL, marketing attribution, and AI agent workflows.
4. Final conversation — culture and team fit.
5. Paid trial period — short, real work, paid.
6. Offer.