Senior Data Analyst – Customer Success Operations

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. Build and maintain recurring reporting, Tableau/Superset dashboards, and data models .

Posted 10/9/2026full-timeRemote • SpainSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining BI dashboards, statistical modeling, and predictive analytics while effectively communicating complex data insights to non-technical stakeholders. Proficient in SQL and Python, with a strong understanding of AI capabilities and experience in cross-functional collaboration.

Highest-signal resume keywords

SQLPythonStatistical ModelingTableauA/B Testing

ATS Keywords

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Hard Skills

SQLPythonStatistical ModelingRegression AnalysisForecastingA/B TestingData AnalysisPredictive ModelingData VisualizationAutomation

Soft Skills

CommunicationCollaborationProblem-SolvingNarrative FramingStakeholder Engagement

Tools & Technologies

TableauLookerBigQueryGitHubAI/LLM

Industry Keywords

Quantitative AnalysisData ScienceBusiness IntelligenceTech CompaniesStart-Ups

Tech Stack

Tools & technologies

BigQueryPythonSQLTableau

About the role

Key responsibilities & impact

Build and maintain recurring reporting, Tableau/Superset dashboards, and data models
Build and maintain automations that scale across markets
Ensure outputs follow BI dashboard and data standards
Review, update, and improve statistical and predictive models
Build MVPs for new predictive or statistical use cases and hand validated models to Data Science
Apply regression, forecasting, and other statistical techniques
Use AI to query, code, and analyse efficiently and consistently
Use and contribute to shared libraries, reusable code, and GitHub-based architecture
Translate business-line needs into requirements for BI, Data Platform, and Product Analytics
Partner with the assigned business-line VP and leadership team to identify gaps and opportunities
Lead deep dives explaining why results occurred and what to do next
Propose, run, and analyse A/B tests and other experiments
Triage incoming requests, sequence workload, and align priorities with business and data stakeholders
Escalate cross-team or resourcing trade-offs to the analytics manager
Report to the Global CS Ops analytics manager

Requirements

What you’ll need

Degree in a quantitative field such as Engineering, Statistics, Mathematics, Physics, or similar
3–5 years of relevant analytics experience as an individual contributor
Very good SQL skills; BigQuery preferred
Solid Python, especially for statistical and predictive modelling
Working knowledge of statistical modelling, including regression analysis, forecasting/prediction, and A/B testing
Hands-on experience with Looker and/or Tableau, building dashboards for non-technical audiences
Ability to digest complex data into business language and frame findings as a clear narrative
Experience working cross-functionally with non-technical stakeholders
Professional fluency in English; Spanish is a plus
Understanding of AI/LLM capabilities and limitations, with experience building things with them
Track record of scoping problems, conducting high-quality quantitative analysis, and developing decision-driving insights
Experience building or validating statistical/predictive models and handing them off to a Data Science team is an extra advantage
Experience in tech companies, start-ups, and/or consultancy is an extra advantage
Must already have the legal right to work in the country of residence or role location
Visa sponsorship is not available

Benefits

Comp & perks

Healthcare insurance
Gym memberships
Mental health support
Vacation, birthday, and recharge time off
Meal vouchers, depending on location
Transport allowances, depending on location
Extended parental leave, depending on location
Career growth and development opportunities
Cross-functional project opportunities
Remote work and flexible hours
Access to office hubs in Barcelona, Warsaw, Curitiba, Rio de Janeiro, Mexico City, Bogotá, Munich, Rome, or Bologna
Global, diverse, and inclusive working environment