Data Engineer

Fanatics Commerce; Sunnyvale (TX); Hybrid; USD 162,000 - 182,000; Full time

Job summary

Fanatics is building a scalable data platform to empower analytics across its sports commerce ecosystem. The role focuses on designing and maintaining data pipelines and a centralized data layer that ingests data from APIs, databases, and partner systems.

Ideal candidates have a Bachelor's degree and 4+ years of experience with SQL, Python/PySpark, ETL, data modeling, and BI tools like Tableau, QuickSight, or Looker. The job is hybrid in Los Angeles with on-site work when not telecommuting.

Qualifications

• Bachelor’s degree plus 4 years of professional experience in data science or analytics.
• Experience translating business requirements into analytical solutions.
• Experience writing complex SQL queries and data mining in data warehouses.
• Experience building data pipelines with Python and ETL processes.
• Experience modeling data and creating curated datasets for analytics.
• Experience designing BI dashboards with SQL and visualization tools.

Responsibilities

• Design, build, and maintain scalable data pipelines using orchestration frameworks.
• Develop a centralized data platform integrating diverse data sources.
• Model data and create curated datasets for analytics.
• Enable self-service data access with data services and semantic layers.
• Collaborate with data scientists, analysts, and business stakeholders.
• Maintain monitoring, logging, and alerting for pipelines; apply CI/CD practices.

Skills

Cross-functional collaboration

Data storytelling

Statistical analysis

Problem solving

Education

Bachelor’s degree in CS/Engineering/related

Tools

Git

Python

PySpark

Pandas

SQL

Tableau

QuickSight

Looker

AWS

ETL

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.

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