Senior Analytics Engineer

We believe in the life-changing impact youth sports have on and off the field because they encourage leadership, teamwork, responsibility, and confidence—important life lessons that have the power to propel our youth toward meaningful futures. We recognize that without coaches, parents, and volunteers, organized youth sports could not exist. By building the first and best place to experience the youth sports moments important to our community, we are helping families elevate the next generation through youth sports.

So if you love sports and their community-building potential, or building cool products is your sport, GameChanger is the team for you. We are a remote-first, dynamic tech company based in New York City, and we are solving some of the biggest challenges in youth sports today.

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.

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  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.

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  Design data validations to ensure data integrity throughout our pipelines, and assure the accuracy of our reporting.

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  Create transparency throughout the full data pipeline by establishing foundational processes with upstream data producers and downstream data consuming tools (BI, Reverse ETL, experimentation).

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  Advance agentic tooling and other automation efforts that help the team spend less time investigating data issues, and more time doing analyses.

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  Apply software engineering best practices like version control and continuous integration to the analytics code base.

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  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.

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  Support cross functional core metrics and data dependencies with leadership visibility.

• 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.

• Direct work with the tools in our data tech stack: Airflow, Github Actions, AWS, Snowplow, Braze, Fivetran, DBT, Snowflake, BigQuery, Looker, Hex, Kubit, Statsig (or equivalents).

• Comfortable productionizing agentic tooling and automating analytics workflows

• Significant modeling and architecture experience with web and app-based subscription data or front-end event-tracking data (e.g., Snowplow, Segment)

• Experience optimizing query performance within a data lakehouse architecture and/or with materialized views.

• Familiarity with creating common Software as a Service (SAAS) metrics.

• Familiarity modeling data from low latency and real-time data pipelines.