If you’re passionate about building scalable data platforms and transforming large datasets into meaningful business insights, this is an exciting opportunity to explore. Fanatics is hiring a Data Engineer in New York, offering a competitive salary ranging from $118,000 to $156,000 per year. You’ll collaborate with cross-functional teams to design reliable data pipelines, optimize data infrastructure, and support analytics that power one of the world’s leading digital sports platforms.
Job Overview
| Field | Details |
|---|---|
| Company Name | Fanatics |
| Role | Data Engineer |
| Qualification | Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field (or equivalent practical experience) |
| Job Location | New York, USA |
| Salary | $118,000–$156,000 per year |
| Work Type | Hybrid / On-site (based on team requirements) |
| Job Type | Full-Time |
| Job Level | Entry Level |
| Industry | Sports Technology, E-commerce, Data & Analytics |
Job Description
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
Selection Process
- Submit the online application.
- Initial resume screening by the recruitment team.
- Recruiter phone interview.
- Technical assessment or coding exercise.
- Technical interview with the engineering team.
- Final interview with hiring manager and key stakeholders.
- Reference and background verification.
- Offer rollout and onboarding.
How to Apply
- Visit the official Fanatics Careers website.
- Search for the Data Engineer – New York position.
- Review the job description, qualifications, and responsibilities.
- Prepare an updated resume highlighting your relevant data engineering experience and technical skills.
- Complete the online application form.
- Upload your resume and any additional requested documents.
- Submit your application and monitor your email for updates from the recruitment team.
