Senior Analytics Engineer

Role overview

Join GameChanger as a Senior Analytics Engineer on the Analytics Hub team. You’ll collaborate with cross-functional partners to scale the data platforms that power decision-making, automation, and measurement. The role focuses on building a reliable data foundation for reporting, analysis, and experimentation across first- and third-party data.

Key missions

• Architect and maintain the data foundation for reporting, analysis, and experimentation
• Transform warehouse data into scalable, self-serve models and artifacts
• Design data validations to ensure integrity and accuracy across the pipelines
• Own data models and transformations for finance and product-centric use cases (e.g., subscription forecasting and sport-specific features)
• Apply software engineering best practices and build foundational models with product, finance, and analytics stakeholders

Responsibilities

• Build and maintain modern data models and transformation layers
• Use event tracking and product analytics patterns (e.g., funnels, user paths)
• Develop and operationalize semantic layers and self-service capabilities for non-technical users
• Optimize and scale warehouse/lakehouse performance while balancing costs
• Communicate clearly with stakeholders about technical requirements and recommendations

Requirements

• 4+ years experience as an analytics engineer / data engineer / data analyst (Product, Finance, or Operations)
• Hands-on experience with dbt, including validations, macros, and selecting materializations
• Strong dimensional modeling skills and experience designing data for scale
• Complex SQL and experience building ad-hoc data pipelines
• Python for data analysis
• Familiarity with event tracking and product analytics (funnels, user paths)
• Comfort working in an agile, iterative, remote-first environment
• Version control with Git
• Demonstrated ability to proactively improve data warehouse architecture

Nice-to-haves / additional stack exposure

• Experience with semantic layers and self-serve tooling (e.g., dbt semantic views, LookML)
• Experience with data validation and transparent pipeline quality controls
• Query performance optimization in data lakehouse architectures (e.g., materialized views)
• Experience with subscription/web/app event data (e.g., Snowplow/Segment)
• Familiarity with SaaS metrics, low-latency/real-time pipelines
• Exposure to tools such as Airflow, GitHub Actions, AWS, Snowplow, Braze, Fivetran, DBT, Snowflake, BigQuery, Looker, Hex, Kubit, Statsig (or equivalents)
• Productionizing and automating analytics workflows (including “agentic tooling”)