Jobs at Fanatics

Build and operate production data pipelines using Python, SQL, Airflow, Snowflake, and Databricks. Implement ingestion sources, DAGs, schemas, monitoring, and catalog registration; investigate and resolve pipeline issues; review code; trace data lineage; support data security, governance, and reverse ETL integrations; collaborate with stakeholders; and mentor junior data engineers.

Lead the design, development, testing, deployment, and maintenance of scalable promotions and rewards software. Guide engineering teams, make architecture and technical trade-off decisions, troubleshoot production issues, improve operational excellence, conduct code reviews, mentor engineers, and influence engineering best practices. Collaborate with product leaders and stakeholders while participating in planning, documentation, and on-call support. Occasional travel to offices, conferences, events, meetings, and team-building activities is required.

Lead platform engineering strategy and hands-on delivery across highly available cloud infrastructure, CI/CD, developer tooling, security, observability, and incident management. Establish standards for AI-assisted software development, validate AI-generated code, measure productivity and quality, and influence engineering, product, and quantitative teams. Mentor engineers and drive cross-team initiatives focused on scalability, resilience, compliance, and operational excellence.

Own the prediction markets product roadmap from discovery through launch and iteration. Define success metrics, use data to prioritize opportunities, manage tradeoffs, and collaborate with engineering, design, data, growth, and commercial teams to deliver consumer-facing digital products.

Build and maintain full-stack features for Fanatics Markets, including React, Next.js, TypeScript, and backend services in Go or other languages. Design scalable systems for markets, orders, and transactions; integrate APIs; implement experimentation, observability, testing, deployment, and monitoring. Collaborate with product, design, and platform teams, participate in code reviews, and use AI coding tools to deliver secure, reliable, high-quality production software.

Own end-to-end development of scalable full-stack experiences for a real-money prediction and trading platform. Design and implement React, Next.js, TypeScript, and backend services; build experimentation, observability, and deployment capabilities; conduct code reviews; collaborate cross-functionally; and establish effective AI-assisted development standards while helping shape an early-stage product.

Lead end-to-end development of high-performance, fault-tolerant backend services for a CFTC-regulated exchange. Responsibilities include modernizing Java systems, building order processing and market data services, managing trading data and reporting pipelines, ensuring observability and regulatory auditability, participating in 24/7 on-call, and applying AI coding tools while maintaining security, correctness, and reliability.

Leads platform engineering initiatives across AWS cloud infrastructure, Kubernetes, CI/CD, GitOps, Infrastructure as Code, observability, and developer tooling. The role architects reliable and scalable platforms, improves developer productivity and operational excellence, partners with application teams, leads cross-team technical programs, mentors engineers, and participates in on-call rotations.

Designs and maintains highly available cloud-native infrastructure, CI/CD pipelines, developer tooling, platform security, and observability systems. The role partners with engineering and product teams to deliver scalable solutions, leads incident response, evaluates AI-generated code for security and architectural risks, and mentors engineers. Candidates will use AI coding collaborators, infrastructure-as-code, Java and Python, and modern cloud platforms to improve developer productivity and operational reliability.

Build and operate production data pipelines using Python, SQL, Airflow, Snowflake, and Databricks. Implement ingestion sources, DAGs, schemas, monitoring, catalog registration, reverse ETL, and governance controls. Investigate pipeline failures, perform lineage analysis, review code, identify downstream risks, and support data quality, security, and access controls. Collaborate with stakeholders, clarify requirements, document escalations, and mentor junior data engineers.