• Build and operate the Data Governance framework • Implement and maintain data quality checks • Own the monitoring & remediation loop • Drive adoption of Data Governance tooling • Scale the program through AI and automation

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions. • Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines). • Understanding Microsoft Dynamics as a data source a big plus. • Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data. • Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability. • Knowledge of Fabric capacity (CU) optimization a big plus. • Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform. • Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data. • Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency. • Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery. • Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

• Analyze sales, repair, and upgrade performance data to identify trends, opportunities, and risks across the US & Canada field organizations • Design and develop dashboards, reports, and data visualizations that translate complex data into clear, actionable insights for Sales, Field Operations, and Leadership • Develop working fluency in the elevator business: unit profiles, component knowledge, contract basics, and the KPIs that drive rep behavior • Build and maintain data models that support long ‑ term strategy for repair revenue growth, customer conversion, and sales productivity • Conduct deep ‑ dive analytics on customer behavior, portfolio performance, and service history to recommend targeted upgrade and repair strategies • Support business case development for new repair programs, pricing initiatives, and process improvements using data ‑ driven insights • Ensure data accuracy, governance, and integrity across multiple sources • Contribute to metadata documentation efforts • Evaluate business processes to identify areas for AI workflow optimization

Role Description 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. What You’ll Do: - 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. Qualifications - 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. Requirements - 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. Benefits - Work remotely throughout the US* or from our well-furnished, modern office in Manhattan, NY. - Unlimited vacation policy. - Paid volunteer opportunities. - Technology stipend - $4,000 every 2 years after your start to make sure you have the latest and greatest technology. - WFH stipend - $500 annually to make your WFH situation comfortable. - Monthly physical, mental, wellness & learning stipend offered through Holisticly. - Monthly lifestyle stipend offered through Fringe. - Full health benefits - medical, dental, vision, prescription, FSA, HRA, HSA, and coverage for family/dependents. - Retirement savings - Traditional and Roth 401K plans are offered through Vanguard, with an immediate company match. - Life insurance - basic life, supplemental life, and dependent life. - Disability leave - short-term disability and long-term disability. - Company paid parental leave - up to 20 weeks for birthing parents and up to 12 weeks for non-birthing parents. - Family building benefits offered through Progyny. - DICK'S Sporting Goods and their family of brands teammate discount.