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 clarifying questions, writes clear PR descriptions, and escalates blockers with useful context rather than staying stuck silently
A growth mindset: takes review feedback well, improves processes, and champions best practices to avoid technical debt

Preferred But Not Required

Exposure to cloud data warehouses/lakehouses (Snowflake, Databricks, AWS) and data catalog/lineage tooling
Familiarity with dbt, Tableau, MongoDB, or PostgreSQL
Familiarity with reverse ETL tools or patterns (e.g., Segment, LaunchDarkly, Kafka, S3-based delivery)
Exposure to PII masking, data security, or RBAC/access governance concepts
Exposure to observability/monitoring tooling (e.g., Datadog) for pipeline health and alerting
Background in gaming, betting, e-commerce, or another regulated/high-compliance industry
Familiarity with responsible handling of customer/PII-sensitive data

Why Join Us

Join a team that's literally described as "the foundation" — everything at FBG, FES, and FMX runs on the data we ingest, govern, and deliver
Learn from senior and staff data engineers on a well-invested, modern data platform, with a clear growth path from DE2 into independent ownership
Work on high-visibility, high-trust systems: regulatory and financial reporting, PII security, and data governance that the business depends on
A culture built around clear tenets: standardize before you scale, own the outcome (not just the ticket), and clarity over complexity
Collaborative culture with strong engineering practices, code review, and mentorship

Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE—a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process.

For information about our benefits, please visit https://benefitsatfanatics.com/

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