Sr. Data Analyst, Compliance - Kraken

About Kraken

Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.

We're looking for a Sr. Data Analyst, Compliance to join our team remotely. The primary goal of this role is to elevate decision-making by uncovering emerging trends and root causes, driving insights and actions that provide full visibility into business performance and operational efficiency.

In this role, you'll own end-to-end data management, from building and maintaining datasets and pipelines to designing dashboards and delivering insights for the Compliance team. A core part of this role is owning the logic and rule-setting behind transaction monitoring across deposits and withdrawals. You'll also contribute to experimentation infrastructure, laying the groundwork for future ML and intelligence initiatives.

The Opportunity

• 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.

What You Bring

• 7+ years of experience in data analysis, data science, and data management within fintech, crypto, financial services, or gambling environments.
• Experience working with compliance, fraud, funding, finance, and customer support teams—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 workflow orchestration, plus 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 (e.g., Hex, Tableau, Power BI) to build executive-facing dashboards.
• Ability to investigate production issues independently and communicate technical findings to non-technical stakeholders.
• Based in Canada, the US, or Brazil, and fluent in English.
• A degree in an analytical field such as analytics, software engineering, statistics, or economics.

Nice to Haves

• Background in fraud detection, AML, anomaly detection, or risk applications.
• Experience with Athena, Trino, Kafka, and Kubernetes alongside the core Airflow/dbt/S3 stack.
• Expertise in transaction pattern analysis, customer segmentation, and detection threshold tuning.
• Experience developing or productionizing machine learning models, including experiment tracking and performance monitoring.
• Working with sparse or delayed feedback from investigations to evaluate and improve detection.
• Knowledge of explainability, auditability, and change control in regulated environments.