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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer II - (Remote) - **Company:** Fanatics Inc - **Location:** New York, NY, United States (Remote available) - **Experience:** Starter - **Salary:** $118,000.0 - $156,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Amazon S3, Unit Testing, Cloud Database, Code Review, Information Engineering, Data Integrity, Extract Transform Load (ETL), Data Security, Identity and Access Management, Python (Programming Language), PostgreSQL, MongoDB, Role-Based Access Control, SQL Databases, Tableau (Software), Workflow Management Systems, Datadog, Data Ingestion, Snowflake, Technical Debt, Git, Apache Kafka, Data Delivery, Software Version Control, Data Pipelines, Databricks - **Published:** July 22, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=54fa68f69cf2083f ## About the Role * 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 ## Description 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., * 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 ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)