• Collaborate with team members to collect business requirements, define successful analytics outcomes and design data models while considering non-functional requirements such as performance and cost constraints and security policies. • Configure ELT tools, integration platforms and orchestration systems to best practice, supporting the development of a modern cloud data warehouse that is accurate, reliable, high‑performing, and easily accessible to business teams. • Supplement the data assets created with supporting documentation to allow these assets to be easily reused by other team members and teams as necessary. • Coordinate with infrastructure and data governance teams to align with and contribute to the company data strategy. • Prioritize quality and data accuracy across all operations and deliverables.

• Collaborate with team members to collect business requirements, define successful analytics outcomes, and design data models while considering non-functional requirements such as performance constraints, cost constraints, and security policies. • Model, design, develop, test, and implement appropriate backend and front-end structures needed to meet business visualization and reporting requirements. • Configure SaaS tools such as ELT providers, integration platforms, and orchestration tools to meet system requirements to build a modern cloud data warehouse that is accurate, reliable, high-performing, and easily accessible to our business departments. • Provide day-to-day support of the data warehouse and associated data marts, including establishing trusted data sets for common data with supporting documentation. • Coordinate with the platform data engineering team, data analyst teams, data governance teams, and IT teams to align with change management processes and to ensure performance processes and access is optimal for global reporting solutions. • Improve processes regarding data flow and quality to improve data accuracy, viability, and value including testing small chunks of data transformations for correspondence to the set tasks. • Understand business/department needs and goals and create data-driven analyses and insights to support management decisions.

• Own end-to-end internal ELT data pipelines: from API ingestion all the way to self-serve dashboards. • Build and maintain canonical data models and metric definitions (for product adoption, usage, and marketing metrics). • Build dashboards and shared analytics tools to support decision making, data-driven content, and self-serve BI. • Collaborate with Product, Marketing, and other internal teams to translate business questions into data requirements. • Ensure data quality, consistency, and governance of data pipelines.

• Work with clients as a participant in their BigQuery and Looker analytics journey • Understand client needs from an organizational, process, and technological perspective • Lead interactive working sessions and conduct hands-on workshops with client technical teams • Design, implement, and maintain data models in Looker, ETL pipelines, and build actionable dashboards