Data Engineer

Fanatics Collectibles•Los Angeles, TX

9d•Hybrid

About The Position

Fanatics is hiring a Data Engineer to design, build, and maintain scalable data pipelines and a centralized data platform. The role integrates data from APIs, databases, and third-party systems; transforms raw data into curated datasets; and enables reliable downstream analytics and business use. It also supports self-service data access through data services, semantic layers, and AI-powered tools. The position collaborates with data scientists, analysts, and business stakeholders to define requirements and deliver scalable, production-grade data solutions. Partial telecommuting is permitted; the employee must work onsite at 11601 Wilshire Blvd., Los Angeles, CA 90025 when not telecommuting. Up to 5% domestic travel may be required for internal meetings. Salary is $162,300–$182,300 per year.

Requirements

Bachelor’s degree or U.S. equivalent in Computer Science, Computer Engineering, Engineering Management, or a related field.
At least 4 years of professional experience as a Data Scientist, Data Analyst, or in another role performing data-driven software engineering and analytics development.
4 years of experience collaborating with cross-functional stakeholders, including business, product, engineering, and data teams, to translate requirements into scalable analytical solutions.
4 years of experience applying software engineering best practices to analytical codebases, including Git version control, peer code reviews, and modular code development.
3 years of experience writing complex SQL queries and mining data across large relational or cloud-based data warehouse environments.
3 years of experience developing Python data pipelines, including use of Pandas and PySpark, and ETL processes to transform and prepare large datasets for analytical use.
3 years of experience developing and optimizing data models, custom datasets, and data structures for evolving analytical and reporting needs.
3 years of experience developing business intelligence dashboards and automated reporting using SQL and visualization tools such as Tableau, QuickSight, or Looker.
3 years of experience maintaining and optimizing cloud-based data solutions within AWS environments.
3 years of experience conducting statistical analyses, including hypothesis testing, regression analysis, and product experimentation.

Responsibilities

Design, build, and maintain scalable data pipelines using orchestration frameworks such as Airflow and Dagster for reliable ingestion and transformation across upstream systems.
Develop and manage a centralized data platform integrating diverse sources, including APIs, databases, and third-party systems, while ensuring data quality, consistency, and availability.
Implement data modeling and ETL/ELT transformation processes to create curated datasets optimized for analytics and business use cases.
Enable self-service data access by building data services, semantic layers, and AI-powered tools such as agents and query interfaces.
Collaborate with data scientists, analysts, and business stakeholders to define data requirements and deliver scalable, production-grade solutions.
Design and maintain monitoring, logging, and alerting systems for pipeline reliability, performance, and data integrity.
Apply software engineering best practices, including CI/CD, version control, and testing, to develop and deploy data infrastructure and pipeline code in a cloud environment.
Collaborate with business, product, engineering, and data stakeholders to translate business requirements into scalable analytical solutions.
Develop and optimize data models, custom datasets, and data structures for analytical and reporting needs.
Design and develop business intelligence dashboards and automated reporting for KPI tracking and executive reporting.
Maintain and optimize cloud-based data solutions in AWS for performance, reliability, and scalability.
Conduct statistical analyses, including hypothesis testing, regression analysis, and product experimentation, to inform business decisions.