Senior Analytics Engineer , GameChanger

• 4+ years of experience in the Product, Finance, or Operations space as an analytics engineer, data analyst, data engineer, or equivalent
• Ability to write complex SQL, ad-hoc data pipelines, and experience in using Python for data analysis
• Hands-on experience implementing modern data modeling strategies with DBT, including validating data, building macros, and selecting optimal materialization
• Proven record of proactively driving improvements to data warehouse architecture
• Comfort with event tracking data and product analytics tools, including familiarity with common event data analyses like funnels and user paths
• Experience scaling a data platform’s capabilities while balancing warehouse costs
• Deep understanding of dimensional modeling techniques and building data for scale
• Comfort working in an agile, iterative environment and ability to thrive in a remote first organization
• Experience building and maintaining semantic layers and self-service technologies (e.g. dbt semantic views and LookML) for non-technical end-users
• Demonstrated capacity to clearly and concisely communicate complex business activities, technical requirements, and recommendations amongst cross-functional stakeholders
• Fluent in version control using git

Desirable

• GameChanger is seeking a Senior Analytics Engineer to join the Analytics Hub team
• Our mission is to deliver easy-to-use, extensible data models and self-serve capabilities that enable stakeholders across the company to answer data questions quickly and confidently
• You’ll collaborate with Analytics, Data Engineering, and cross-functional partners in Finance, Product and Engineering to scale the platforms that power decision-making, automation, and measurement
• In this role, you will work with both first- and third-party data to architect and maintain the data foundation for reporting, analysis, and experimentation
• Using Python, SQL, and DBT, you’ll transform warehouse data into scalable, self-serve data models and artifacts (e.g. , metrics, dashboards) that unlock insights and empower teams across Finance, Product and Engineering
• Own architecting, optimizing, and transforming finance and product centric data models in DBT for flexible analysis by Data Analysts / Scientists and self-service by business stakeholders
• Design data validations to ensure data integrity throughout our pipelines, and assure the accuracy of our reporting
• Create transparency throughout the full data pipeline by establishing foundational processes with upstream data producers and downstream data consuming tools (BI, Reverse ETL, experimentation)
• Advance agentic tooling and other automation efforts that help the team spend less time investigating data issues, and more time doing analyses
• Apply software engineering best practices like version control and continuous integration to the analytics code base
• Work closely with our Data Engineers, Analysts, Data Scientists, Diamond (Baseball/Softball) Sports, and Finance teams to build foundational models around new sport specific features, monthly recurring revenue, and subscription forecasting
• Support cross functional core metrics and data dependencies with leadership visibility