Sr. Data Analyst, Compliance

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, and by driving insights and actions that provide full visibility into business performance and operational efficiency. Beyond technical expertise, success in this role depends on being a true business partner who combines data-driven insights with strategic thinking to deliver actionable recommendations, guide decision-making, and drive measurable impact across the organization. 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.

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, 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; ML exposure is a strong plus; 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; Belief in the values outlined in Kraken's Tentaclements document; 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

Nice to have

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