Data Engineer II - (Remote)

Fanatics is building a leading global digital sports platform. They are seeking a Data Engineer II to join their Data Engineering team, where the role involves implementing data ingestion pipelines and supporting data governance efforts under the guidance of senior engineers.

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