• Take ownership of meaningful metrics and data products, from definition through adoption. • Build and maintain dbt models that define how we measure the business. • Design and maintain dashboards used by growth, finance, and operations to make day-to-day decisions. • Improve data quality through testing, monitoring, and validation. • Enable self-serve analytics so teams can answer their own questions and move faster.

• Structure and evolve the corporate analytics layer, transforming data already available on the platform into reliable, organized, metrics-oriented assets. • Analytics Modeling & Metrics: Develop and evolve dimensional and metric-driven models. • Define and standardize corporate KPIs. • Build data marts oriented to business domains. • Define and maintain a semantic layer for consumption by BI tools and analytical applications. • Document business rules and metric calculations. • Solution and Data Product Development: Design and evolve data products based on certified metrics. • Create analytics-ready datasets for consumption. • Establish reliable data foundations to support AI initiatives and predictive models. • Collaborate with business areas in designing data-driven solutions. • Quality, Governance & Performance: Implement quality tests in the analytics layer. • Ensure traceability and consistency of metrics. • Ensure adequate performance for analytical consumption. • Strategic Engagement: Work closely with Product, Operations and Finance to define strategic metrics.

• dbt at Fora: project structure, conventions, CI/CD, tests, contracts, documentation, and performance. • Data models across bronze, silver, and gold layers. • The semantic layer (dbt MetricFlow or equivalent): metric definitions, dimensions, governance, and adoption. • Production operation of the transformation layer: jobs, dependencies, failures, retries, alerts, environments, and release hygiene. • Python tooling for validation, dbt utilities, lightweight automation, and integrations. • Data quality, test coverage, and observability for modeled tables. • Partnership with Risk & Analytics to formalize business metrics currently spread across SQL, Tableau, and analyst knowledge.

• Design, build, and maintain scalable data models in Amazon Redshift to support reporting, dashboards, and ad hoc analysis. • Collaborate with data engineering teams to expand and optimize the analytics warehouse architecture. • Establish and enforce standards for schema design, naming conventions, and data lineage. • Develop, maintain, and optimize data pipelines that deliver clean and reliable data to business users. • Partner with stakeholders to gather requirements and translate business needs into scalable data solutions. • Create and maintain comprehensive data documentation, including metrics definitions and transformation logic. • Support BI platforms by ensuring analytics-ready datasets and assisting with dashboard development. • Monitor data quality and implement testing, alerting, and validation processes to ensure warehouse reliability. • Identify opportunities to improve data accessibility, consistency, and usability across the organization. • Promote best practices in analytics engineering, including version control, code reviews, and reproducible workflows.