• Translate campaign requirements into segmentation logic, SQL queries, and executable workflows • Execute campaign code and generate list pulls using established frameworks • Validate campaign outputs through reconciliation checks, exploratory analysis, and edge case detection • Perform statistical checks to confirm segment distributions, thresholds, and suppression logic • Write, optimize, and maintain SQL queries for large-scale extraction and analysis • Support ETL workflows in SSIS (Visual Studio), including job monitoring and troubleshooting • Use Tableau and Looker for data validation and analytical slicing (not dashboard development) • Identify opportunities to automate campaign execution and validation to improve reliability and reduce risk • Ensure secure delivery and compliance through SFTP transfers, encryption/decryption, and governance standards

• Lead experimentation strategy: Design, execute, and analyze A/B tests and quasi-experiments to evaluate product and feature impact on engagement, retention, and customer satisfaction. • Drive product insights: Conduct deep-dive analyses on user journeys, onboarding funnels, feature adoption, and retention cohorts to identify growth and optimization opportunities. • Define and operationalize metrics: Establish north-star metrics, KPIs, and guardrails; ensure consistent definitions across teams and dashboards. • Enable product decision-making: Translate complex analyses into clear, actionable recommendations for product roadmaps and prioritization. • Improve data foundations: Partner with data engineering and platform teams to ensure high-quality telemetry, scalable data models, and reliable reporting layers. • Leverage advanced analytics: Apply statistical techniques (segmentation, cohort analysis, regression, causal inference) to uncover drivers of user behavior and product performance. • Collaborate cross-functionally: Work closely with Product Managers, Designers, and Engineers to embed analytics into the product development lifecycle. • Mentor and elevate analytics practices: Guide analysts on experimentation design, metric definition, and storytelling best practices. • Communicate effectively: Deliver clear, compelling insights to stakeholders and leadership through dashboards, presentations, and narratives.

• Understand business area needs and translate them into technological solutions. • Build and maintain databases for internal consumption. • Develop integrations with financial market APIs (ANBIMA, data providers, among others). • Create processes for data collection, processing, and consolidation. • Develop internal tools to support investment management. • Design automations and operational workflows using Python. • Build dashboards and analytical views to support decision-making. • Use Artificial Intelligence resources as productivity accelerators and to generate business value. • Work with large volumes of data in complex environments. • Ensure development best practices, governance, and access management.

• Partner with business stakeholders, analysts, and engineers to assess reporting needs and develop effective data solutions. • Gather and translate business requirements into data specifications, ensuring alignment with organizational goals. • Design and develop various reporting solutions, including dashboards, scorecards, and tabular data visualizations. • Create and maintain reports using SQL Server Reporting Services (SSRS) and deploy them via Visual Studio and Report Builder. • Write, optimize, and manage SQL queries to support complex reporting requirements. • Analyze report requests to ensure they meet business needs, identifying opportunities for optimization and efficiency. • Identify and eliminate redundant or unnecessary data points in reports to enhance clarity and usability. • Validate and reconcile data discrepancies across multiple sources to maintain data integrity. • Troubleshoot and resolve issues related to reports, queries, and analytics. • Maintain a catalog of existing reports and ensure proper documentation. • Conduct complex data mining exercises, interpreting results through various analytical methods. • Provide support by resolving user-reported data and reporting issues. • Offer recommendations to management for process improvements and performance metrics. • Participate in quality assurance (QA) testing and project implementation efforts.