Data Engineer II

About the Team

Launched in 2021, Fanatics Betting and Gaming is the online and retail sports betting subsidiary of Fanatics, a global digital sports platform. The Fanatics Sportsbook is available to 95% of the addressable online sports bettor market in the U.S. Fanatics Casino is currently available online in Michigan, New Jersey, Pennsylvania and West Virginia. Fanatics Betting and Gaming operates twenty-two retail sports betting locations, including the only sportsbook inside an NFL stadium at Northwest Stadium. Fanatics Betting and Gaming is headquartered in New York with offices in Denver, Leeds and Dublin.

About the Role

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. 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

Nice to Have

• 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

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.

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