• Own and expand our semantic layer • Lead semantic layer development in dbt as the single source of truth for metrics across dashboards, reports, AI tools, and embedded analytics • Partner with the Senior Data Engineer on data architecture and quality testing for data products • Deprecate legacy reporting methods and drive adoption of the semantic layer as the default • Translate technical concepts and internal terminology into business language so data is intuitive for non-technical users • Build analytics products that drive business value • Lead refreshes and new builds of internal and partner-facing analytics products that help us identify risks and surface opportunities • Support embedded analytics work that brings customer-facing dashboards directly into the product • Partner with Partner Success, Product, and Leadership to gather requirements and design analytics products that answer the question behind the question • Make data trusted, accessible, and easy to use • Own our internal data knowledge base - the centralized source for documentation, metric definitions, and lineage • Create documentation, short-form videos, and dashboard guides that help employees use data confidently • Support cross-functional data power users for knowledge sharing and feedback on data products • Partner on data literacy training and onboarding, including new-hire modules and annual refreshers • Support our AI initiatives • Build and maintain the verified query repository and curated data assets that power our internal AI agents • Monitor agent performance and identify gaps in context or data that can be addressed at the data layer

• Build new analyses and support existing ones using SQL and Python. • Apply software engineering principles like version control and continuous integration to the analytics codebase. • Expand our data warehouse with clean data ready for analysis. • Apply advanced data testing strategies to ensure resulting datastores are aligned with expected business logic. • Implement validation checks and automated testing procedures to manage data quality in your ETL/ELT pipelines. • Work with stakeholders to define business logic and data expectations. • Help drive a change in the usage of data by actively surfacing insights to stakeholders. • Lead initiatives and problem definition, scoping, design, and planning. • Build tools and automation to run data infrastructure. • Manage large-scale data migrations in relational datastores.

• Build and maintain dbt models for cross-domain analytics use cases • Create reusable facts, dimensions, and marts for analytics consumption • Support cross-domain data products such as Churn, LTV, Segmentation, and Customer Intelligence • Implement dbt tests, source freshness checks, and reconciliation logic • Translate business requirements into data models and metric logic • Support data quality checks and model validation • Document model definitions, business rules, assumptions, and lineage • Collaborate closely with analytics and domain teams to ensure datasets are fit for reporting and decision-making

• Support the development of automated data workflows and reporting processes. • Assist in building scripts and tools using Python to improve operational efficiency. • Work with structured and unstructured data to support analytics and automation initiatives. • Collaborate on process optimization and automation projects across teams.