• 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.

• 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.

• Participate in vendor scoping sessions to finalize full table and field requirements before the build phase begins. • Model a dedicated datamart in the dbt transformations marts layer, sourcing data from core order, line item, and customer tables. • Translate vendor schema specifications into specific internal field mappings, accurately accounting for DTC-adjusted logic such as ship-to addresses, order dates, and customer grain. • Configure Snowflake data sharing and connections to facilitate seamless vendor ingestion. • Write comprehensive dbt tests and documentation for all delivered models to ensure long-term data quality and transparency. • Manage the full merge request workflow using GitLab, adhering to strict peer review processes before deployment.