Sr. Data Analyst, Compliance

Kraken is seeking a Senior Data Analyst, Compliance to improve decision-making and operational visibility through data-driven insights. The role owns data pipelines, transaction-monitoring detection logic, evaluation and deployment of detection workflows, and analytics for the Compliance team.

Responsibilities:

• Own the logic and rule-setting behind transaction monitoring across deposits and withdrawals, partnering closely with Compliance to study patterns and refine detection logic
• Translate compliance requirements into production detection logic, preserving correctness across alert eligibility, thresholds, customer segmentation, date windows, and deduplication
• Develop and maintain Python and SQL pipelines for transaction data preparation, feature engineering, and detection workflows
• Evaluate detection rules and analytical models across coverage, alert quality, threshold sensitivity, and the impact of proposed changes
• Design reproducible evaluations on historical data that account for limited labels, class imbalance, and shifts in customer behaviour over time
• Own testing, deployment, scheduling, monitoring, and operational support for detection workflows, including investigating data quality issues, pipeline failures, and unexpected changes in detection results
• Execute historical backfills with clear validation and traceability, and maintain reliable downstream alert delivery and integrations with compliance systems
• Be at the forefront of data innovation by experimenting, testing hypotheses, and translating insights into action, while incorporating software engineering best practices into pipelines and dashboards

Requirements:

• 7+ years of experience in data analysis, data science, and data management within the fintech, crypto, financial services, or gambling industry, gained in a fast-paced, high-growth environment
• Experience working with compliance, fraud, funding, finance, and customer support teams and analyzing large datasets, engineering features, and applying statistical methods to practical business problems
• Strong Python and SQL skills, with experience building and supporting production data or analytical systems
• Hands-on experience using dbt or similar tooling to model and organize high-fidelity data for reporting and decision-making
• Experience setting up and managing data pipelines with a strong understanding of workflow orchestration, plus strong software engineering practices including version control, automated testing, code review, and CI/CD
• Solid statistical foundation
• Proficiency with BI and data visualization tools (e.g., Hex, Tableau, Power BI, or similar) to build executive-facing dashboards
• Ability to investigate production issues independently and communicate technical findings and uncertainty to non-technical stakeholders
• A degree in a field emphasizing analytical rigor, such as analytics, software engineering, statistics, or economics
• Based in Canada, the US, or Brazil, and fluent in English
• ML exposure is a strong plus
• Fraud detection, AML, anomaly detection, or other risk applications
• Experience with Athena, Trino, Kafka, and Kubernetes alongside the core Airflow/dbt/S3 stack
• Transaction pattern analysis, customer segmentation, and detection threshold tuning
• Developing or productionising machine learning models, including experiment tracking and performance monitoring
• Working with sparse or delayed feedback from investigations to evaluate and improve detection
• Explainability, auditability, and change control in regulated environments