Sr. Data Analyst, Compliance

Kraken

2d•$110,400 - $220,800•Remote

About The Position

Payward is seeking a Sr. Data Analyst, Compliance to work remotely and elevate decision-making by identifying emerging trends and root causes, providing visibility into business performance and operational efficiency, and delivering actionable recommendations. The role partners with Compliance and other teams to guide decisions and drive measurable organizational impact. The analyst owns end-to-end data management for the Compliance team, including datasets, pipelines, dashboards, and insights. A central responsibility is defining and maintaining transaction-monitoring logic for deposits and withdrawals. The role also contributes to experimentation infrastructure that can support future machine-learning and intelligence initiatives, while applying software engineering best practices to production pipelines and dashboards.

Requirements

At least 7 years of experience in data analysis, data science, and data management within fintech, crypto, financial services, or gambling, in a fast-paced, high-growth environment.
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, including 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 a strong understanding of workflow orchestration.
Strong software engineering practices, including version control, automated testing, code review, and CI/CD.
A solid statistical foundation.
Proficiency with BI and data visualization tools such as Hex, Tableau, Power BI, or similar, including building 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 Haves

Machine-learning exposure.
Experience with 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.
Experience developing or productionizing machine-learning models, including experiment tracking and performance monitoring.
Experience working with sparse or delayed feedback from investigations to evaluate and improve detection.
Experience with explainability, auditability, and change control in regulated environments.

Responsibilities

Own transaction-monitoring logic and rules for deposits and withdrawals, working with Compliance to study patterns and refine detection logic.
Translate compliance requirements into production detection logic, maintaining 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 for coverage, alert quality, threshold sensitivity, and the impact of proposed changes.
Design reproducible historical-data evaluations that account for limited labels, class imbalance, and changes in customer behavior over time.
Own testing, deployment, scheduling, monitoring, and operational support for detection workflows.
Investigate data-quality issues, pipeline failures, and unexpected changes in detection results.
Execute historical backfills with validation and traceability, and maintain downstream alert delivery and integrations with compliance systems.
Build and maintain datasets and pipelines, design dashboards, and deliver insights for the Compliance team.
Experiment, test hypotheses, and translate insights into action; contribute to experimentation infrastructure for future ML and intelligence initiatives.