Note: The job is a remote job and is open to candidates in USA. Payward, the parent company behind Kraken, is seeking a Senior Data Analyst, Compliance to support the Compliance team. The role owns transaction-monitoring logic, data pipelines, and detection workflows, and delivers evaluations and insights to improve compliance decisions and operational efficiency.
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
Skills
- 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
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