Data Engineer II - (Remote)

Fanatics is building a global digital sports platform across commerce, collectibles, and betting and gaming. The Data Engineer II will implement and support reliable data ingestion pipelines, Airflow workflows, security and governance processes, and data delivery integrations under the guidance of senior engineers. The role also involves troubleshooting pipeline issues, collaborating with stakeholders, and developing data engineering best practices.

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

Requirements:

• 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
• 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