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Data Analyst, Regulatory Analytics and Reporting

Jobgether

BrazilYesterday

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Skills

airflowamlauditcompliancedata analysisdata modelingdata pipelinesdata qualitydbtkycoperational risk managementpandaspythonregulatory reportingrisk assessmentsqlworkflow orchestration

About the role

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Analyst, Regulatory Analytics and Reporting based in Brazil.

Join a global financial technology environment where data analytics plays a critical role in regulatory compliance, risk management, and strategic decision-making. In this role, you will transform complex compliance data into reliable insights that support regulatory reporting and business objectives. You will work at the intersection of data analytics, engineering, and compliance operations, ensuring accuracy, transparency, and audit readiness. By developing scalable data models, automated workflows, and insightful dashboards, you will help streamline processes and reduce operational risk. You will collaborate closely with compliance specialists, engineers, analysts, and senior leadership to address complex regulatory challenges. This is an opportunity to apply your technical expertise and regulatory knowledge in a fast-evolving digital asset industry.

Accountabilities

    • Develop regulatory reporting and dashboards: Design, build, and maintain dashboards, compliance metrics, and regulatory reports that deliver actionable insights to stakeholders, regional compliance officers, and senior leadership.

    • Automate compliance processes: Create end-to-end data solutions, governance controls, and automated workflows that improve operational efficiency, reduce manual intervention, and minimize human error.

    • Build scalable data models: Analyze large and complex datasets and transform findings into reliable, well-documented, audit-ready data models that support regulatory reporting and business analytics.

    • Develop data pipelines: Translate regulatory and business requirements into accurate reports and automated data workflows using SQL, Python, dbt, Airflow, and related technologies.

    • Collaborate across teams: Lead and contribute to cross-functional initiatives involving data analysts, data engineers, and compliance teams to develop and maintain high-quality data sources and pipelines.

    • Monitor data quality: Identify data inconsistencies, investigate issues, coordinate remediation efforts, and work with responsible teams to ensure timely resolution.

    • Support audits and regulatory reviews: Provide accurate and timely data for audits, regulatory examinations, risk assessments, and day-to-day compliance operations.

    • Communicate actionable insights: Present analytical findings through clear, data-driven storytelling that helps stakeholders understand regulatory risks, evaluate performance, and make informed decisions.

    • Requirements

      • Professional experience: At least 5 years of experience in data analysis and data management within financial services or another regulated industry, including hands-on work with compliance analytics and regulatory reporting.

      • Regulatory knowledge: Practical experience with compliance frameworks and requirements such as Anti-Money Laundering (AML), Know Your Customer (KYC), sanctions screening, MiCA, or MiFID, along with a strong understanding of regulatory risk and audit-ready environments.

      • Advanced SQL skills: Expertise in complex queries, joins, common table expressions (CTEs), analytical functions, and extracting insights from large datasets.

      • Python proficiency: Experience using Python and analytical libraries such as pandas, matplotlib, and Plotly for data processing, analysis, and visualization.

      • Data modeling expertise: Hands-on experience with dbt or comparable tools to build, organize, and maintain reliable datasets for reporting and decision-making.

      • Data pipeline management: Experience designing, implementing, and maintaining automated data pipelines, with a solid understanding of workflow orchestration and data dependencies.