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Kraken, one of the world's longest-standing crypto platforms with over 10 million users, is seeking a Senior Data Analyst for Compliance to join remotely. The role focuses on elevating decision-making through data-driven insights and operational efficiency across the compliance function.
You will own end-to-end data management for the Compliance team, including building and maintaining datasets, pipelines, dashboards, and delivering actionable insights. A core responsibility is owning the logic and rule-setting behind transaction monitoring across deposits and withdrawals, working closely with the Compliance team to study patterns and refine detection logic.
Key responsibilities include:
- Translating compliance requirements into production detection logic, ensuring correctness across alert eligibility, thresholds, customer segmentation, date windows, and deduplication
- Developing and maintaining Python and SQL pipelines for transaction data preparation, feature engineering, and detection workflows
- Evaluating detection rules and analytical models across coverage, alert quality, threshold sensitivity, and impact of proposed changes
- Designing reproducible evaluations on historical data accounting for limited labels, class imbalance, and behavioral shifts
- Owning testing, deployment, scheduling, monitoring, and operational support for detection workflows
- Executing historical backfills with clear validation and traceability
- Experimenting with data innovation, testing hypotheses, and translating insights into action while incorporating software engineering best practices
You will serve as a true business partner, combining data-driven insights with strategic thinking to guide decision-making and drive measurable impact across the organization.
REQUIREMENTS:
- 7+ years of experience in data analysis, data science, and data management within fintech, crypto, financial services, or gambling industry in fast-paced, high-growth environments
- Experience working with compliance, fraud, funding, finance, and customer support teams; analyzing large datasets; engineering features; and applying statistical methods to business problems
- Strong Python and SQL skills with experience building and supporting production data or analytical systems
- Hands-on experience with dbt or similar tooling to model and organize high-fidelity data for reporting and decision-making
- Experience setting up and managing data pipelines with strong understanding of workflow orchestration
- Strong software engineering practices including version control, automated testing, code review, and CI/CD
- Solid statistical foundation; ML exposure is a strong plus
- Proficiency with BI and data visualization tools (Hex, Tableau, Power BI, or similar) to build executive-facing dashboards
- Ability to investigate production issues independently and communicate technical findings to non-technical stakeholders
- Degree in analytical field such as analytics, software engineering, statistics, or economics
- Based in Canada, US, or Brazil; fluent in English
NICE TO HAVES:
- Fraud detection, AML, anomaly detection, or other risk applications
- Experience with Athena, Trino, Kafka, Kubernetes alongside Airflow/dbt/S3 stack
- Transaction pattern analysis, customer segmentation, and detection threshold tuning
- Developing or productionizing machine learning models with 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
