Sr. Data Analyst, Compliance

Role overview

Own data analysis and production workflows that help a compliance team understand business performance and improve monitoring of deposits and withdrawals. The role combines detection-rule design, pipeline and dataset management, historical evaluation, dashboards, and close partnership with compliance and other business teams.

Responsibilities - Translate compliance needs into transaction monitoring rules, maintaining correct eligibility, thresholds, customer segments, date windows, and deduplication. - Build and maintain Python and SQL pipelines for transaction preparation, feature engineering, and detection workflows. - Assess rules and analytical models for coverage, alert quality, threshold sensitivity, and the effects of proposed changes. - Design repeatable evaluations on historical data that account for sparse labels, class imbalance, and changing customer behavior. - Own testing, deployment, scheduling, monitoring, backfills, and operational support; investigate data quality or pipeline issues and maintain reliable alert integrations. - Build datasets and dashboards, share actionable findings, and contribute to experimentation foundations for future machine learning work.

Requirements - At least seven years in data analysis, data science, or data management in fintech, financial services, crypto, or gambling. - Strong Python and SQL skills, with experience building and supporting production analytical systems. - Experience with dbt or similar data modeling tools and workflow orchestration. - Solid statistical skills and experience analyzing large datasets with business, compliance, fraud, or customer support teams. - Familiarity with software engineering practices such as version control, automated testing, code review, and CI/CD. - Proficiency with BI tools for dashboards, clear communication of technical findings, a relevant analytical degree, and fluent English; applicants must be based in Canada, the United States, or Brazil.

Nice to have - Experience with fraud, AML, anomaly detection, transaction pattern analysis, segmentation, or threshold tuning. - Familiarity with tools such as Airflow, S3, Athena, Trino, Kafka, or Kubernetes; production ML, sparse investigation feedback, or regulated change control experience.