Chief Data Officer

What you’ll do

Global Data Platform & Infrastructure

Define and execute the strategy for Trustly’s global data platform, including data lakehouse architecture, streaming and batch pipelines, data cataloging, observability, and correctness.

Own the end-to-end data engineering function, ensuring reliable, scalable, and cost-efficient data infrastructure that serves product, analytics, risk, and compliance use cases across all geographies.

Drive the evolution of Trustly’s data stack, making deliberate build-vs.-buy decisions and managing a best-in-class ecosystem of data tools and vendors.

Establish and enforce data standards, taxonomy, and governance frameworks that enable self-service access without sacrificing data quality or integrity.

Partner with Engineering and Infrastructure teams to ensure the data platform meets the availability, security, and regulatory requirements of a globally operating payments company.

Data Strategy & Key Datasets

Identify, develop, and steward Trustly’s highest-value proprietary datasets, including transaction-level payment data, bank account intelligence, consumer behavioral signals, and merchant performance data.

Define a long-term data strategy that turns Trustly’s unique data assets into durable competitive advantages - for risk management, product differentiation, and potential new revenue streams.

Oversee data governance, data lineage, and master data management programs, ensuring a single source of truth across the organization.

Champion data quality and reliability as foundational standards, implementing frameworks that ensure confidence in data used for business-critical decisions.

Collaborate with Legal, Compliance, and Privacy teams to ensure responsible and lawful use of data across all jurisdictions, including under GDPR, CCPA, and relevant open banking data regulations.

Analytics & Business Intelligence

Build and lead a world-class analytics function that delivers actionable insight to business stakeholders across Product, Finance, Risk, Operations, Sales, and the C-suite.

Establish a self-service analytics culture, enabling non-technical teams to access, explore, and act on data confidently and safely.

Own the development of key performance metrics, executive dashboards, and board-level reporting, ensuring data is accessible, accurate, and impactful at every level of the organization.

Partner with merchant-facing teams to develop external analytics products and insights that deepen Trustly’s value proposition with key partners and enterprise clients.

Machine Learning & Artificial Intelligence

Lead Trustly’s machine learning and AI program, setting the technical vision and organizational structure for applied ML across fraud detection, risk scoring, payment success optimization, personalization, and operational automation.

Build and grow a team of machine learning engineers and data scientists, establishing rigorous practices for model development, validation, deployment, and monitoring.

Drive the responsible adoption of generative AI and large language models where they can create meaningful productivity or product value, with appropriate governance and oversight.

Define and maintain Trustly’s AI ethics and model risk management framework, ensuring fairness, explainability, and regulatory compliance across all deployed models.

Stay current with the rapidly evolving AI/ML landscape and translate emerging capabilities into concrete business opportunities for Trustly.

Leadership & Cross-Functional Partnership

Recruit, develop, and lead a high-performing, global team of data engineers, analytics engineers, data scientists, ML engineers, and BI analysts.

Foster a data-driven culture across Trustly, partnering with senior leaders to raise the overall level of data literacy and analytical rigor in decision-making.

Represent Trustly’s data capabilities externally with partners, regulators, and the broader industry, where relevant.

Contribute to the broader technology leadership team, collaborating with the CTO and peer executives on cross-cutting strategy and resource allocation.

Who you are

15+ years of progressive experience in data engineering, data science, analytics, or a closely related technical discipline, with at least 5 years in a senior leadership role.

Demonstrated success building and scaling data platforms and teams in high-growth, data-intensive technology or financial services environments.

Deep expertise in modern data architecture: cloud data warehouses (Snowflake, BigQuery, Redshift), data lakes, streaming platforms (Kafka, Flink), and orchestration frameworks.

Hands-on understanding of machine learning and AI methodologies, with experience overseeing applied ML programs in production at scale.

Strong command of data governance, data quality, and regulatory compliance requirements relevant to financial services and global data privacy law (GDPR, CCPA).

Track record of translating complex data strategy into business outcomes, with the communication skills to influence C-suite and Board-level audiences.

Experience managing geographically distributed, cross-functional data teams in a global organization.

Prior CDO, VP of Data, or equivalent executive title at a technology company, payments platform, or regulated fintech.

Experience in payments, open banking, financial services, or another highly regulated, data-rich industry.

Familiarity with transaction-level payment data, fraud modeling, credit risk scoring, or consumer behavioral analytics in a financial context.

Proven experience building or scaling ML platforms and MLOps infrastructure for production model deployment and monitoring.

Experience with generative AI and large language model deployment in an enterprise context.

Advanced degree (M.S. or Ph.D.) in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience.