Senior Analytics Engineer, Topps Digital

Fanatics Collectibles•Los Angeles, NY

1d•$140,120 - $193,750

About The Position

The Senior Analytics Engineer at Topps Digital, part of Fanatics Collectibles, bridges raw data and business decisions. The role owns work ranging from BI dashboards to AI-powered insights and partners directly with functional leads to shape how the business understands performance and makes decisions. The successful candidate is self-motivated and intellectually curious, challenges assumptions, and brings innovative thinking to complex business questions. The role leads cross-functional analytical initiatives from ambiguous business problems through scoping and analysis to decision-ready recommendations. It also builds and maintains dashboards, automated reporting, and AI-driven insight tools in the Databricks ecosystem; defines KPI methodologies and supporting pipelines and visualizations; and standardizes reporting across teams. In addition, the role develops a knowledge repository and supporting logic, evaluations, and guardrails for a business AI chat agent. Topps Digital powers mobile-first digital collecting experiences for sports and entertainment brands, including MLB, NFL, NBA, Global Football, WWE, UFC, Disney, Marvel, and Star Wars. The salary range is $140,120–$175,150 for Los Angeles and $155,000–$193,750 for New York City; these ranges represent base pay only and may not apply to other locations. Final compensation considers location, experience, qualifications, and training. Benefits information is available at https://benefitsatfanatics.com/.

Requirements

At least 6 years of experience in analytics engineering, business intelligence, or a related field.
Advanced SQL skills, including independently writing, quality-assuring, and optimizing complex queries against large, imperfect datasets.
Experience building and maintaining dashboards in Tableau, Power BI, QuickSight, Looker, or similar BI tools.
Hands-on experience with cloud data infrastructure, including AWS services and Databricks (such as Delta Lake, Databricks SQL, and workflow orchestration).
Demonstrated experience owning complex, cross-functional projects end-to-end, from an ambiguous request to stakeholder-ready recommendations.
Experience integrating AI tools into analytics workflows.
Strong organizational skills and ability to manage multiple projects simultaneously.
Experience with project management tools such as Asana or Jira, and familiarity with agile frameworks.
Command of statistical methods such as experimental design and hypothesis testing, regression analysis, causal inference, time-series forecasting, multivariate analysis, segmentation and clustering, and Bayesian methods, applied through structured business-analysis frameworks.
Strong stakeholder management and communication skills, including presenting to leadership and senior stakeholders, explaining complex problems clearly, and proposing alternative approaches when warranted by the data.
High standards for data quality and accuracy, including catching issues before they surface in leadership reporting.

Nice To Haves

Experience with Python, R, or relevant statistical programming languages.
Background in e-commerce, sports, entertainment, and/or mobile gaming industries.
Genuine interest in Fanatics, collectibles, mobile apps and gaming, and sports.

Responsibilities

Lead complex, cross-functional analytical initiatives end-to-end, from ambiguous business problems through scoping and analysis to decision-ready recommendations.
Conduct analyses to validate business opportunities and support key decisions, including scenario analysis and impact sizing.
Apply statistical rigor and structured problem-solving to explain business outcomes and recommend actions.
Present findings to stakeholders and senior leadership, and defend analytical methodology.
Diagnose root causes in data and translate findings into concrete, actionable recommendations.
Build and maintain dashboards, automated reporting, and AI-driven insight tools within the Databricks ecosystem.
Optimize and automate operational workflows, reducing recurring manual data pulls and repetitive decision-making.
Define KPI definitions and methodologies, and build the underlying pipelines and visualizations.
Standardize reporting and unify processes across cross-functional teams.
Build and maintain a source of truth for internal business questions to support decisions.
Build and maintain a knowledge repository for the business AI chat agent.
Design and build logic that enables the chat agent to answer business questions accurately.
Build evaluation frameworks and guardrails that determine when the chat agent can operate independently and when it should defer to human review.