Sr. Data Analyst, Compliance

Accountabilities • 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.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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