Data Engineer

Fanatics Collectibles; Sunnyvale (TX); On-site; USD 162,000 - 182,000; Full time

Benefits offered by this job

Partial telecommuting

On-site LA office

Job summary

Fanatics Collectibles is seeking a Data Scientist to design and maintain scalable data pipelines and a centralized analytics platform. You will integrate diverse data sources, build ETL/ELT processes, and empower self-service analytics for business stakeholders in a cloud environment.

The role requires collaboration with data scientists and analysts, strong SQL and Python skills, and experience with AWS and BI dashboard tools.

Qualifications

• 4+ years of experience building analytical solutions with cross-functional stakeholders.
• 4 years collaborating with business, product, engineering, and data teams.
• 3 years writing complex SQL and data mining in cloud data warehouses.
• 3 years building data pipelines with Python ETL/ELT.
• 3 years developing data models and datasets for analytics.
• 3 years designing BI dashboards and automated reports.

Responsibilities

• Design, build, and maintain scalable data pipelines using Airflow and Dagster.
• Develop a centralized data platform integrating diverse data sources for analytics.
• Implement robust ETL/ELT processes to structure data for reporting.
• Enable self-service data access via semantic layers and data services.
• Collaborate with data scientists, analysts, and business stakeholders on data needs.
• Design and maintain monitoring, logging, and alerting for pipelines.
• Apply software engineering best practices (CI/CD, Git, testing) in cloud environments.
• Up to 5% domestic travel for internal meetings; telecommuting available; on-site LA office.

Skills

Python

SQL

ETL/ELT

Airflow

Dagster

Data Modeling

BI Dashboards

AWS

Git

Pandas PySpark

Education

Bachelor's degree in CS/Engineering/Related field

Tools

Tableau

Looker

QuickSight

Job description

About Us

Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports properties, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.

Design, build, and maintain scalable data pipelines using orchestration frameworks such as Airflow and Dagster to support reliable data ingestion and transformation across multiple upstream systems 35%; Develop and manage a centralized data platform that integrates diverse data sources (e.g., APIs, databases, third-party systems), ensuring data quality, consistency, and availability for downstream consumption 20%; Implement robust data modeling and transformation processes (ETL/ELT) to structure raw data into curated datasets optimized for analytics and business use cases 15%; Enable self-service data access by building data services, semantic layers, and AI-powered tools (e.g., agents, query interfaces) that allow business users to easily explore and consume data 10%; Collaborate with cross functional teams (data scientists, analysts, and business stakeholders) to define data requirements and ensure scalable, production-grade data solutions 10%; Design and maintain monitoring, logging, and alerting systems to ensure pipeline reliability, performance, and data integrity 5%; Apply software engineering best practices (CI/CD, version control, testing) to develop and deploy data infrastructure and pipeline code in a cloud-based environment 5%. Up to 5% domestic travel may be required for internal meetings. Partial telecommuting permitted; on-site at 11601 Wilshire Blvd., Los Angeles, CA 90025 when not telecommuting. Salary: $162,300 - $182,300 per year.

MINIMUM REQUIREMENTS

Bachelor's Degree or U.S. equivalent in Computer Science, Computer Engineering, Engineering Management, or a related field, plus 4 years of professional experience as a Data Scientist, Data Analyst, or any occupation, job title, or position performing data-driven software engineering and analytics development.

Must also have experience in the following: 4 years of professional experience collaborating with cross functional stakeholders (including business, product, engineering, and data teams) to translate business requirements into scalable analytical solutions; 4 years of professional experience applying software engineering best practices to analytical codebases, including version control (Git), peer code reviews, and modular code development; 3 years of professional experience writing complex SQL queries and performing data mining across large relational or cloud-based data warehouse environments; 3 years of professional experience designing and developing data pipelines using Python (including Pandas and PySpark) and ETL processes to transform and prepare large datasets for analytical use; 3 years of professional experience developing and optimizing data models, custom datasets, and data structures to support evolving analytical and reporting needs; 3 years of professional experience designing and developing business intelligence dashboards and automated reporting solutions SQL and visualization tools (including Tableau, QuickSight, or Looker) to support KPI tracking and executive reporting; 3 years of professional experience maintaining and optimizing cloud-based data solutions within AWS environments to ensure performance, reliability, and scalability; 3 years of professional experience conducting statistical analyses, including hypothesis testing, regression analysis, and product experimentation to inform business decision-making.

By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy.

Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview.