Senior Analytics Engineer, Topps Digital

Fanatics Collectibles•Los Angeles, CA

2d•$140,120 - $193,750

About The Position

As a Senior Analytics Engineer at Topps Digital, part of Fanatics Collectibles, you will bridge raw data and business decisions, owning work spanning BI dashboards to AI-powered insights. The role calls for a self-motivated, intellectually curious person who challenges assumptions, asks why, and brings innovative thinking. You will act as a thought leader and partner directly with functional leads to shape how the business understands performance and makes decisions. Topps Digital powers digital collecting experiences across sports and entertainment brands, including MLB, NFL, NBA, Global Football, WWE, UFC, Disney, Marvel, and Star Wars. Its digital collectible apps bring Topps' trading-card heritage into a mobile, digital-first format. You will lead complex analytical initiatives, develop decision-ready recommendations, build and maintain dashboards and automated reporting in the Databricks ecosystem, and support an AI chat agent with knowledge resources, answer logic, evaluation frameworks, and guardrails. The listed base salary ranges are $140,120–$175,150 for Los Angeles and $155,000–$193,750 for New York City. These ranges are location-specific and exclude short- and long-term incentive compensation. Final base pay considers factors including 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, QAing, 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 the Databricks platform, such as Delta Lake, Databricks SQL, and workflow orchestration.
Demonstrated experience owning complex, cross-functional projects end-to-end, from ambiguous requests 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, including experimental design and hypothesis testing, regression analysis, causal inference, time-series forecasting, multivariate analysis, segmentation or clustering, and Bayesian methods, applied through structured business-analysis problem-solving frameworks.
Strong stakeholder management and communication skills, including presenting to leadership and senior stakeholders, translating complex problems clearly, and proposing alternative approaches when warranted by the data.
High standards for data quality and accuracy, including catching issues before they appear 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 from an ambiguous business problem through scoping, analysis, and a decision-ready recommendation.
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 why business outcomes are occurring and recommend actions.
Present findings to stakeholders and senior leadership and defend analytical methodology under scrutiny.
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 business workflows to reduce recurring manual data pulls and repetitive manual decision-making.
Define KPI definitions and methodology, then 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 decision-making.
Build and maintain a knowledge repository for the business's AI chat agent.
Design and build the logic that enables the chat agent to answer business questions accurately.
Build evaluation frameworks and guardrails to determine when the chat agent can operate independently and when it should defer to human review.