Data Engineer II - (Remote)

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. About the Team We're looking for a Data Engineer II to join our Data Engineering team, which builds and governs the data foundation that powers the business. You'll work within our stack — Python ingestion pipelines, Airflow orchestration, and Snowflake/Databricks — helping move data reliably and securely from source to decision-ready output. This is an entry-level role. You'll execute well-defined tasks under the direction of senior data engineers, learn our team's stack and conventions, and build a strong foundation in pipeline correctness. You're not expected to own designs independently yet — you're expected to build reliable software against a design, ask good questions, and grow quickly from feedback. Responsibilities Implement ingestion pipelines and Airflow DAGs from a senior engineer's design, using the team's scaffolding and conventions — including writing the code, unit tests, and documentation Support data security and governance work, such as PII masking and access controls, following established patterns Contribute to data delivery work, including reverse ETL integrations, under guidance from senior engineers Add and extend fields in existing pipelines, incorporating review feedback and applying learned patterns on future work Take oncall pages for pipeline failures, work through runbooks, and escalate with clear context when needed Pair with senior engineers on data integrity issues you can't yet diagnose alone Write clear, reviewer-friendly PR descriptions and ask clarifying questions before starting new work Flag blockers early and with context rather than going quiet when stuck Build strong working relationships with internal stakeholders (BI analysts, other data engineers, data scientists) and help gather and clarify requirements Conduct and participate in code and system inspections Help the team define and adhere to data engineering best practices Mentor more junior data engineers as you grow into the role Experience and Skills 1–3 years of professional software or data engineering experience A self-learner with a strong ability to gather, evaluate, and analyze requirements Solid foundation in Python and deep understanding of SQL and ETL/ELT for complex data transformations Comfort reading and writing unit tests, and working within an established codebase and conventions Familiarity with (or eagerness to quickly learn) workflow orchestration tools like Airflow (Managed Workflows for Apache Airflow) Basic understanding of data pipeline concepts: ingestion, idempotency, scheduling, and data quality Knowledge of several of the following technologies: Snowflake, Databricks, AWS, dbt, Tableau, MongoDB, PostgreSQL Familiarity with Git-based version control and PR-based code review workflows Strong communication skills — asks clar…