[Hiring] Data Engineer III @Fanatics Betting & Gaming

Role Description

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.

You'll implement features and fixes against a given design, handle known classes of pipeline issues on your own, and escalate genuinely novel problems with clear context rather than working them in isolation. You're also expected to start contributing meaningfully in code review — catching real bugs, not just style nits.

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.
• 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 during spec review.
• Write clear handovers when escalating genuinely unresolved issues — detailing what you tried, what you ruled out, and where things diverge.
• 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.
• 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.

Qualifications

• 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.

Benefits

• 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.