Top Data Analyst Jobs in Madrid

Partner with Product teams to analyze user journeys, run A/B tests, build dashboards, extract insights using SQL, Tableau and Amplitude, and collaborate with Data Platform engineers to drive data-informed product decisions.

Analyze mobile games data to track performance, design and execute A/B tests, report on user engagement, and recommend optimizations.

Design, build, and maintain Power BI dashboards and Microsoft Fabric data pipelines. Integrate Dynamics 365 data, ensure data quality and governance, deliver executive insights, forecasting, and strategic analysis. Partner with sales, operations, and finance leaders to translate requirements, promote self-service analytics, and document KPIs and reporting logic.

Embedded with a product squad as the PMs' dedicated data partner, lead deep cross-product analyses focused on activation and adoption, design and evaluate experiments, improve instrumentation and event taxonomies, surface retention and cohort insights, and translate rigorous statistical analyses into actionable product recommendations.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Early-career Data Analyst responsible for designing and owning core metrics and dashboards, analyzing transaction/customer/credit data to inform credit, fraud, AML, portfolio and growth decisions, building data pipelines and automations with SQL and Python, partnering with cross-functional teams, and owning compliance reporting. Emphasis on turning ambiguous problems into reliable analytical systems and applying GenAI/LLMs for practical automation.

Analyze and instrument offsite discovery and SEO/GEO performance for ecommerce sites. Build integrations, ETL pipelines, and models to track organic and AI-driven traffic. Create tooling and frameworks for technical site audits, landing page modelling, SEO experiments, and enablement for product and customer teams.

Analyze and improve multilingual classifiers, partner with linguistics and engineering teams, validate taxonomy translations, and document findings.

The Tableau Developer will design, build, and maintain Tableau dashboards, optimizing performance and ensuring data accuracy while collaborating with stakeholders.

The Senior Data Analyst (CX) will analyze customer experience data, optimize workflows, and collaborate with teams to improve customer support through insights and reporting.

Manage the operational lifecycle of the Flight Plan for e-commerce clients ensuring proper setup, maintenance, and actionable insights from the infrastructure.

As a Senior Data Analyst, you will analyze data, create reports, manage data pipelines, and provide insights to influence product decisions. You will collaborate with various teams and perform A/B testing.

Lead end-to-end analytics projects, deliver actionable insights and KPIs, present to senior leadership, improve data quality and governance, apply statistical methods, and uplift analytics across the business.

Perform hands-on data analysis and support EMEA commercial and sales teams. Build and maintain Power BI dashboards and reports, optimize SQL queries, support data modeling and ELT processes, manage Power Automate workflows, ensure reporting accuracy, handle ad-hoc requests, and collaborate with senior analysts while contributing to process improvements and learning.

Analyze large-scale user and product data, design and evaluate A/B tests, define and validate product and business metrics, build dashboards and reports, and partner with engineers, data scientists, product, and customers to translate insights into product improvements.

Lead and improve LCA database content and models, develop Data-on-Demand datasets, support customers on MLC database topics, triage JIRA issues, and promote AI/tool use to optimize data management.

Use SQL and Python to analyse transaction monitoring and fraud rule performance, identify patterns and anomalies, reduce false positives, and prioritise high-risk cases. Partner with FinCrime, Product, Compliance and Data teams to improve controls, data pipelines and detection logic, support scoring and future ML use cases, and translate analysis into actionable recommendations that balance fraud prevention with customer friction.

The Director Analyst focuses on data integration and engineering, providing insights, advice, and research to help organizations improve their data practices and strategies.