Sr. Data Analyst, Compliance

• Transaction Monitoring: Own and refine the logic and rules used to monitor deposits and withdrawals, collaborating with compliance stakeholders to identify transaction patterns and improve detection effectiveness.

• Detection Logic Development: Translate compliance requirements into reliable, production-ready detection logic, ensuring accuracy across alert eligibility, thresholds, customer segmentation, date windows, and duplicate handling.

• Data Pipeline Engineering: Develop and maintain Python and SQL pipelines for transaction data preparation, feature engineering, and detection workflows.

• Detection Performance Evaluation: Assess detection rules and analytical models based on coverage, alert quality, threshold sensitivity, and the potential impact of proposed changes.

• Historical Analysis and Testing: Design reproducible evaluations using historical data, accounting for limited labels, imbalanced datasets, and evolving customer behavior.

• Production Operations: Manage testing, deployment, scheduling, monitoring, and ongoing support for detection workflows, investigating data quality issues, pipeline failures, and unexpected results.

• Data Backfills and Integration: Execute historical data backfills with appropriate validation and traceability, ensuring reliable downstream alert delivery and integration with compliance systems.

• Analytics and Business Insights: Analyze large datasets, investigate root causes and emerging trends, and deliver actionable recommendations that improve business performance and operational efficiency.

• Reporting and Visualization: Build and maintain dashboards and analytical outputs that provide clear visibility into compliance performance and support data-informed decision-making.

• Continuous Improvement: Apply software engineering best practices across pipelines and dashboards while experimenting with new approaches, testing hypotheses, and contributing to future machine learning and intelligence capabilities.

Requirements

• Professional Experience: At least 7 years of experience in data analytics, data science, or data management, ideally within fintech, cryptocurrency, financial services, or the gambling industry in a fast-paced, high-growth environment.

• Cross-Functional Collaboration: Experience working with compliance, fraud, funding, finance, or customer support teams to solve practical business problems using data.

• Python and SQL: Strong proficiency in Python and SQL, with hands-on experience developing, maintaining, and supporting production-grade data or analytical systems.

• Data Modeling: Experience with dbt or similar tools for structuring, transforming, and maintaining reliable datasets for reporting and decision-making.

• Pipeline Orchestration: Experience building and managing data pipelines, with a solid understanding of workflow orchestration and dependable data processing practices.

• Software Engineering Practices: Familiarity with version control, automated testing, code reviews, continuous integration and deployment (CI/CD), and production system maintenance.

• Statistical Analysis: A strong statistical foundation and the ability to apply analytical methods, feature engineering, and hypothesis testing to complex business challenges.

• Business Intelligence: Proficiency with data visualization and business intelligence tools such as Hex, Tableau, Power BI, or equivalent platforms, including the development of executive-facing dashboards.

• Problem-Solving and Communication: Ability to investigate production issues independently, interpret complex findings, communicate uncertainty clearly, and present recommendations to non-technical stakeholders.

• Education: A degree in an analytically rigorous discipline, such as analytics, software engineering, statistics, economics, or a related field.

• Language and Location: Fluency in English and current residence in Brazil, Canada, or the United States.

• Additional Advantage — Risk Analytics: Experience in fraud detection, anti-money laundering (AML), anomaly detection, or other risk-related applications.

• Additional Advantage — Data Infrastructure: Familiarity with technologies such as Athena, Trino, Kafka, and Kubernetes, alongside tools such as Airflow, dbt, and S3.

• Additional Advantage — Detection Optimization: Experience analyzing transaction patterns, segmenting customers, tuning detection thresholds, and evaluating systems with sparse or delayed investigation feedback.

• Additional Advantage — Machine Learning: Experience developing or deploying machine learning models, including experiment tracking, performance monitoring, and model evaluation.

• Additional Advantage — Governance: Knowledge of explainability, auditability, and change control within regulated environments.

• Mindset: A strong interest in data-driven compliance, continuous improvement, and the evolving financial technology landscape.

Benefits

• Remote Work: The flexibility to work remotely from Brazil within an internationally distributed organization.

• Meaningful Impact: The opportunity to strengthen compliance and transaction monitoring capabilities in a technology-driven financial services environment.

• Technical Ownership: End-to-end responsibility for analytical systems, detection logic, data pipelines, and production workflows.

• Advanced Analytics: Opportunities to explore statistical methods, detection optimization, experimentation, and emerging machine learning applications.

• Cross-Functional Exposure: Collaboration with compliance, finance, fraud, and other business and technical teams.

• Professional Development: Opportunities to deepen expertise in data engineering, risk analytics, financial technology, and production-grade analytical systems.

• Inclusive Work Environment: A merit-based environment that values diverse backgrounds, perspectives, skills, and professional experiences.

• Skills-Based Assessment: Job-related assessments may be used as part of the recruitment process to evaluate relevant competencies alongside experience and interviews.