Lead Data Analyst

October 18, 2026

Hybrid

Lead

Barcelona

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Mandatory office attendance
2 days per week

Description

Senior individual-contributor role in Marketing Analytics focused on paid search (SEM) analytics for a global portfolio of brands and markets. The person will shape how acquisition is measured and optimized, partnering with SEM leaders, Finance, Data Science, and Analytics Engineering. Based in Barcelona with a hybrid arrangement of two office days per week.

Responsibilities

Guide bidding and investment across brands, evaluating spend allocation, marginal returns, customer lifetime value (LTV), and payback
Help SEM teams assess cross-brand cannibalization and identify where additional spend generates incremental growth
Lead experimentation and incrementality: design and analyze channel tests, geo-split experiments, and multibrand tests
Diagnose changes in acquisition cost, conversion, and return on ad spend (ROAS) and translate findings into clear recommendations
Strengthen attribution and reporting: reconcile platform and internal measurement, build trusted dashboards
Own the SEM analytics roadmap, prioritize questions by business impact, and lead stakeholders toward evidence-based decisions
Raise the team's analytical standards: mentor analysts, review SQL and analytical methods, and develop reusable tools and documentation

Requirements

6+ years of analytics experience with a track record of turning complex analysis into business decisions and measurable outcomes
Advanced SQL and strong statistical analysis skills in Python or R, including reviewing others' code and collaborating with engineering on data models
Proven experimental design and A/B testing experience, including applying causal inference or geo-based methods and explaining their limitations
Experience independently leading large analytical initiatives, managing stakeholder expectations and timelines, and influencing strategy across functions
Clear communicator with technical and non-technical audiences, able to shape compelling data narratives through analysis and data visualization
Strong understanding of marketing attribution and acquisition economics, including customer acquisition cost (CAC), LTV, ROAS, and payback
Experience with multibrand businesses, international markets, or a two-sided marketplace
Experience with marketing mix modeling (MMM), customer LTV modeling, or transformation tools such as dbt
Experience using AI tooling to move faster on routine work and to check your own reasoning