Location: Remote — Latin America (LatAm) • Reports to: Head of Analytics (TBD) • Type: Full-time • Created: Jule 2026

🚀 We're building AI agents for performance marketing.

Plurio is the AI agent for media buying teams running $300K–$15M/month on Google, Meta, and TikTok. It connects to your internal backend data and your ad accounts, automates analysis and optimization, saves 50% of time, and boosts ROAS by 10–30%. Watch a five-minute demo.

Plurio transforms how media buying teams work:

1. From manually analyzing dashboards → to the AI agent doing deep data research in minutes.

2. From manually adjusting budget → to the agent suggesting optimal split at channel, campaign, and ad-set level.

3. From manually killing / scaling / relaunching creatives → to agents autonomously performing those actions in ad platforms, based on full-funnel data.

4. From decisions made with gut feeling → to agents optimizing automation rules with historical data and ML models.

Our agents already run $500M+/year in ad spend (see this inside a $20M+/year UA team). Since our public release in March, our customer base has grown 4× in 10–12 weeks.

Our goal — and the mission we'd build together — is the autonomous user acquisition factory that runs 10× better than anything that came before, growing budgets under management to $30B/year.

🚀 The Role

We build AI that does performance-marketing work. To do that, the AI — and the humans steering it — need a rock-solid analytical foundation: clean attribution, end-to-end funnel visibility, and a real understanding of how money actually moves across Meta, Google, and the rest of the stack.
That foundation is what this role owns. You're the person who can look at messy cross-channel data and say not "CTR dropped" but "here's why the unit economics don't work and what to do about it." You understand attribution from the inside — first touch vs. last touch, multi-touch paths, custom windows, LTV-driven optimization for funnels where the purchase doesn't happen on day one.

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.

If owning marketing analytics + attribution and building AI-driven analytical workflows sounds like your kind of problem — read on.

🎯 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 — at a business level, not just a metrics level.

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. Turn that into reallocation and optimization decisions.

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. You're as much a workflow builder as an analyst.

Dashboards & insight delivery
Build clear, accessible dashboards and reporting (Power BI / Looker / Tableau-class) that make insight usable by the team and clients. Pair real-time anomaly detection with sane evaluation windows (7–14 days) so decisions are signal, not noise.

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 — so the product reasons about marketing the way a great analyst would.

🔥 You're the Right Person If

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. You understand performance marketing and web funnels from the inside, not from a course.

— Hands-on experience with AI coding agents — Claude Code, Cursor, Codex or equivalent — and a track record of building workflows and automations with them, not just chatting with a model.

— You think in systems: repeatable pipelines and automated analysis, not manual one-off pulls.

— Proficiency with version control, particularly Git — you version your queries, workflows, and analysis like an engineer, not in scattered files.

— 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 — and where attribution actually breaks in production.

• Experience in segments with complex funnels and LTV-driven models (EdTech, FinTech, Consumer Apps, B2C SaaS, Healthcare, Home Services).

• A track record of owning projects independently — you take a problem, run it to a result, and stand behind the outcome, with minimal hand-holding.

• Public samples we can read — SQL, dashboards, an agent workflow you built, a write-up.

• Russian С1

🚩 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; specifics discussed individually.

• AI-first culture, for real — the whole company works in Cursor with a shared workspace and premium team access to AI tools. You'll have the best agentic tooling in the industry and a team that expects you to use it.

• Maximum impact — your attribution models and analysis directly shape both client outcomes and the AI product itself. Not a corporation where insight drowns in bureaucracy.

• Remote — work from anywhere with reliable US-hours overlap.

🚀 Hiring Process

1. Application — CV + short answers (see below). We aim to respond within 48 hours.

2. Intro call (People & Operations, ~30 min) — company overview, background, motivation, logistics.

3. Technical interview — deep dive on SQL, marketing attribution / cross-channel analytics, and Meta / Google Ads analysis on real scenarios; how you'd build an analytical workflow with an AI agent.

4. Final conversation — culture, ways of working, team fit.

5. Paid trial period — short, real work, paid.

6. Offer — if it's a fit, we align on terms and start date.

How to Apply

Fill out the application form — no cover letter, no formalities. We value specifics.