Postes à Santander, Espagne

The Customer Risk Monitoring team is responsible for implementing and maintaining the analytical intelligence that protects Teya and its customers from financial risks including fraud, money laundering, and terrorism financing. Our goal is to minimise financial losses and risk exposure while supporting and maintaining our customers' trust and ensuring compliance with regulatory requirements.

We work closely with the Operations team to investigate suspicious activities and collaborate cross-functionally with Compliance, Risk, Platform Engineering, and product leaders to build and continuously improve the intelligence powering our detection and monitoring systems.

As a Data Analyst in the Customer Risk Monitoring team, you will:

1. Develop and refine the intelligence used to detect financial crime, balancing efficacy with operational efficiency to reduce false positives, investigation time, and customer friction.
2. Deliver actionable insights that directly improve detection rates, uncover new risk patterns, and inform prevention strategies.
3. Act as a bridge between data and the business, collaborating closely with Operations, Compliance, Engineering, and Product teams to shape and prioritize analytical initiatives.
4. Identify opportunities to improve and automate monitoring, escalate emerging threats, and continuously evolve our understanding of risk.
5. Build and maintain clear and impactful dashboards, reports, and documentation to monitor key metrics, surface relevant trends, and drive data-informed decisions.
6. Partner with data engineering to design and maintain scalable, reliable data models and ETLs that underpin both operational and strategic use cases.
7. Help define and analyze fraud and AML KPIs, assisting the business in planning and course correction with confidence.
8. Promote a culture of data-driven decision-making across the organization.

We are looking for a Senior Data Analyst with a strong analytical foundation and a passion for tackling complex, ambiguous problems in the financial risk domain.

• At least 4 years of professional experience as a data analyst, preferably in fraud, AML, or financial risk monitoring.
• Solid understanding of statistical concepts and experience applying methods such as forecasting, time series analysis, A/B testing, or regression analysis.
• Experience in producing insights that led to measurable improvements, ideally in relevant domains such as fraud, AML detection, or operations.
• Advanced proficiency in SQL and experience working with large, complex, and sometimes messy datasets.
• Experience designing, building, and maintaining ETL pipelines or data models, ideally using tools like dbt.
• Proficiency in Python for data analysis, including data manipulation, visualization, and basic modeling.
• Strong data storytelling and communication skills: ability to translate complex data into clear, actionable recommendations for both technical and non-technical stakeholders.
• Experience working collaboratively with cross-functional partners, including Operations, Compliance, Engineering, and Product teams.
• Self-starter who thrives in a fast-paced, high-ambiguity environment and takes ownership of their work from start to finish.

• Experience in acquiring services or the payments industry.
• Direct experience with problems such as AML scenario tuning, fraud rule optimization, or detection intelligence design.
• Familiarity with regulatory or compliance-driven analytics environments.
• Fincrime certifications such as CAMS, ICA, or equivalent.

The Perks

• Health Insurance
• 25 days of Annual leave + Bank holidays
• Flexible working hours, as long as it suits both you and your team

Remote Work: Yes | Employment Type: Full-time

Microsoft Office, Risk Management, Financial Services, Customer Service, Fraud, Root Cause Analysis, Law Enforcement, Banking, Analysis Skills, Authentication, Qualitative Research, Interviewing, ACH

Years of experience required: 4+

1 position available