Top Data Analyst Jobs in Spain

Lead marketing analytics initiatives, including statistical analysis, campaign optimization, LTV modeling, incrementality analysis, data visualization, and third-party data pipeline development. Partner with marketing and cross-functional teams to identify growth opportunities, deliver actionable insights to senior stakeholders, and prototype AI-driven automation for recurring analytics workflows and improved decision-making.

Support global data team collecting, organizing, and verifying financial data across markets. Assist with acquisition channels, resolve data issues, document client requests, and apply project management practices (LEAN Six Sigma) to improve workflows and meet production deadlines.

Entry-level internship to develop expertise in Morningstar data and products, support data processes, ensure data accuracy and deadlines, assist analysts, and participate in training and projects to build skills for future roles.

Lead game analytics partnering with product teams to drive strategy, player and economy insights, design A/B tests, build predictive models (churn, LTV, segmentation), mentor analysts, and improve data tools and experimentation practices.

As a Principal Data Analyst at Zynga, you will shape game strategies, analyze player behavior, design experiments, mentor analysts, and promote data-informed decision-making to enhance player engagement and business growth.

Manage investment fund data quality, regulatory portfolio reporting, and fund categorization initiatives across global markets. Process and validate managed investment data, resolve complex stakeholder queries, identify automation and process improvement opportunities, and create operational requirements for scalable tools. Serve as a subject-matter expert on investment data, coach junior analysts, coordinate training, and ensure accurate, timely data delivery while supporting continuous improvement through Lean Six Sigma and Agile methods.

Maintain and administer Workday and other HRIS platforms, ensuring data integrity and process quality. Build and maintain complex Workday reports, deliver metrics and analytical insights, investigate data discrepancies, support system upgrades and implementations, and perform functional testing. Translate business requirements into solutions, manage data updates and integrations, coordinate with stakeholders, provide training, and respond to reporting and system-related inquiries.

Support EMEA data team in collecting, maintaining, and validating investment data. Ensure data quality, coverage, and timeliness; assist meeting production deadlines; analyze data points; support project teams and process improvements; liaise with fund companies and third-party providers; organize team activities and use LEAN Six Sigma methodologies for issue resolution.

Build and maintain regulatory reporting data models, validation processes, compliant regulator submissions, dashboards, and visualizations. Partner with Data, Engineering, Finance, Compliance, and Regulatory Reporting teams to translate requirements into accurate, traceable, automated reporting solutions across jurisdictions. Improve reporting workflows and support internal stakeholders and auditors.

Twelve-month internship supporting collection, verification, and delivery of financial data across global markets. Work with global data teams and fund companies to resolve data issues, set up acquisition channels, document client requests, and apply project management practices to improve workflows and data quality.

Processes and validates managed investment data across global markets, ensuring accuracy, completeness, and timely delivery. Collaborates with global teams, fund companies, and internal clients; manages data issues, acquisition channels, specifications, and user stories. The role also improves procedures, develops analytical tools, documents use cases, and supports process improvement initiatives using Lean Six Sigma methodologies.

Lead marketing analytics initiatives, including statistical evaluation of campaign performance, predictive modeling, LTV and incrementality analysis, data pipeline development, and interactive visualization. Partner with marketing and cross-functional teams to identify growth opportunities, communicate actionable insights to senior stakeholders, and prototype AI-driven automation for recurring analytics workflows.

Leads independent data analysis projects, translating business challenges into analytical problems and actionable recommendations. The role analyzes complex datasets, validates insights, develops visualizations and dashboards, and presents findings to business stakeholders. It also facilitates discovery workshops, defines KPIs, documents data flows and business processes, and serves as a liaison between business teams and data engineering to ensure accurate, strategically valuable reporting.

Leads a team of 3–5 data analysts, prioritizes analytical work, and analyzes large real-time bidding datasets to identify market trends and revenue opportunities. Builds self-service BI and automated reporting frameworks, establishes data quality and documentation standards, and partners with Product, Data Science, and Engineering to guide product decisions.

Contribute to the Tech team by analyzing data to inform product roadmap and prioritization, interact with clients and internal teams to understand needs, make data-driven decisions, and lead development ownership of impactful features.

Provide data-driven compliance insights and reporting to Compliance, ML, and Operations teams. Analyze complex datasets with SQL and Excel, build BI dashboards, handle ad-hoc data requests, and collaborate with Product and Data teams to ensure consistent, accurate compliance data.

Own and improve end-to-end reporting pipelines, ensure data quality and scalable automated reporting, partner with stakeholders and engineering to translate business needs into dashboards and analyses, and explore AI-enabled automation for insights generation.

As a Data Analyst, you'll analyze user behavior and product performance, creating reports and dashboards to drive business insights. You'll collaborate with cross-functional teams and utilize SQL, Python, and data visualization tools to enhance user experiences and define key performance indicators.

Analyze and model data to support transaction monitoring and FinCrime investigations. Own end-to-end delivery of analytics solutions, build dashboards, ensure data quality and auditability, support regulatory reporting and automate FinCrime processes.

As a Fraud Data Analyst, you will architect real-time defense strategies against payment fraud, develop precision rules, optimize decisioning stacks, and report performance metrics to senior leadership while ensuring a frictionless user experience.

Build production-grade finance data products: design dbt models on Redshift, create Metabase self-service dashboards, validate reconciliations and provisioning, partner with Finance to ensure auditable, single-source-of-truth metrics, and drive data governance and quality improvements.

Join the global data team to collect, validate, and maintain investment data (Morningstar). Manage provider relationships, meet production deadlines, resolve data tickets, document processes, and drive process improvements using project management methodologies to ensure high data quality and client-focused delivery.

Work with global data and local business teams to acquire, validate, and maintain investment and Morningstar data. Manage provider relationships, meet production deadlines, resolve data tickets, improve procedures and analytical tools, and translate client needs into specifications and user stories using project management methodologies.

Collect, validate, and maintain global investment data (Morningstar), manage fund-provider relationships, resolve data issues, improve processes and tools, and translate client requests into specifications while meeting production deadlines.

Lead data initiatives to improve accuracy, timeliness, and coverage of investment data. Act as an expert on fund structures and methodologies, manage provider relationships and data acquisition, implement LEAN Six Sigma process improvements, ensure high data quality, handle high data volumes against strict deadlines, collaborate cross-functionally to resolve complex issues, and translate client requests into specifications and user stories.