At a Glance
- Tasks: Build and govern data pipelines using Python, Airflow, and Snowflake/Databricks.
- Company: Join Fanatics, a leading global digital sports platform igniting fan passions.
- Benefits: Competitive salary, growth opportunities, and a supportive team environment.
- Other info: Mentorship opportunities and a clear path to senior roles.
- Why this job: Make a real impact on high-visibility systems that power the sports industry.
- Qualifications: 3-5 years in data engineering, strong SQL and Python skills required.
The predicted salary is between 60000 - 80000 £ per year.
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 Role
We're looking for a Data Engineer III 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.
What You'll Do
- Implement new ingestion sources end-to-end against a senior engineer's design — connector code, DAG, schema, monitoring, and catalog registration — extending the team's existing framework where a new source needs a pattern it doesn't yet support.
- Investigate pipeline failures independently, recognize known classes of issues, and ship the documented fix without needing to escalate.
- Before shipping a new pipeline, identify downstream consumers and what would break if data were late or wrong, and flag gaps like this during spec review — before writing code.
- Write clear handovers when escalating a genuinely unresolved issue — what you tried, what you ruled out, and where things diverge — so a senior can pick up without re-discovery.
- Review peers' pipeline PRs and catch non-obvious issues (e.g., missing idempotency checks, race conditions) that could cause incorrect downstream data.
- Turn ambiguous "why is this data wrong?" questions into structured investigations — tracing data lineage from source to warehouse and communicating back what you found.
- Surface concerns in spec review as specific, well-reasoned questions rather than staying silent or blocking progress.
- Support data security and governance work (e.g., PII masking, access controls) and contribute to data delivery work, including reverse ETL integrations.
- Build strong working relationships with internal stakeholders and help scope and clarify requirements for new work.
- Mentor DE2s on their first significant projects — pairing on tricky decisions and helping them apply team conventions.
What We're Looking For
- 3–5 years of professional software or data engineering experience.
- Strong SQL and Python skills, with solid experience building and operating production data pipelines.
- Comfort investigating and root-causing pipeline issues independently before escalating.
- Experience with workflow orchestration tools (Airflow or similar) and a cloud data warehouse/lakehouse (Snowflake, Databricks, or similar).
- Solid understanding of data pipeline concepts: idempotency, schema evolution, backfills, and data quality/testing.
- Experience giving substantive code review feedback, not just style or formatting comments.
- Strong communication skills — can write a clear technical handover, ask sharp questions in spec review, and explain a data lineage investigation to a non-technical stakeholder.
- A track record of taking ownership of known-class problems end-to-end rather than needing step-by-step direction.
Nice to Have
- Experience extending or building reusable pipeline frameworks/templates.
- Exposure to reverse ETL tools or patterns, PII masking, or data access governance (RBAC).
- Exposure to observability/monitoring tooling (e.g., Datadog) for pipeline health and alerting.
- Some experience mentoring or informally supporting more junior engineers.
- Background in gaming, betting, e-commerce, or another regulated/high-compliance industry.
Why Join Us
- Real ownership over known-class problems, with senior/staff support available for the genuinely novel ones.
- Work on high-visibility, high-trust systems 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.
- Clear growth path into Senior Data Engineer, with room to start mentoring and shaping team practices along the way.
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Data Engineer III - FMX in London employer: Fanatics Betting & Gaming
At Fanatics Betting & Gaming, we pride ourselves on fostering a dynamic and innovative work culture that empowers our employees to thrive. Located in the UK, we offer competitive benefits, a collaborative environment, and ample opportunities for professional growth, particularly in the exciting field of AI-driven technology. Join us to be part of a forward-thinking team where your contributions will make a meaningful impact.
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We think this is how you could land Data Engineer III - FMX in London
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
