Full Stack Data Engineer
Cavaliers Holdings LLC
Full-Stack Data Engineer
Cavaliers Holdings LLC is obsessed with delivering epic experiences for our team members, our fans, our community and the world. Fueled by an unapologetically bold, inclusive and high-impact culture, we strive to recruit individuals who are enthusiastic and purposeful in carrying out our vision as transformative leaders in sports and entertainment. If you are ready to bring your best to the dynamic, high‑performance environment of the Cavs, Cleveland WNBA, Monsters, Charge, RESN and Rocket Arena - where we come together to create moments that move, inspire, and uplift - then we want to hear from you.
We're hiring a Full-Stack Data Engineer to join a small, high-trust team building the systems that fuel decisions across the organization. This is a two-sided role by design: you'll build and own the data pipelines that feed our warehouse and the applications and interfaces that put that data in the hands of business users. The systems you build won't stop at a table — they'll go all the way through to something a stakeholder actually uses. If you want to be a pure data engineer forever, this isn't the seat. We want engineers who move fluidly between pipeline work and application work because that's how our team ships end-to-end value.
Essential Duties & Responsibilities:
- Design, build, and maintain production data pipelines feeding our Aurora PostgreSQL warehouse and downstream analytics
- Own dbt models end-to-end: sources, staging, marts, tests, documentation
- Improve pipeline reliability: monitoring, alerting, incident response, root-cause analysis
- Build and maintain internal applications, APIs, and integrations that expose data to business teams in Ticketing, Marketing, Sales, and BIG (Business Intelligence Group)
- Integrate with vendor systems and third-party APIs to move data in and out of the platform
- Partner directly with business stakeholders and analysts to translate a business need into a shipped system
- Choose the right solution (build, buy, configure, automate, or advise), not the most engineering-intensive one
- Contribute to platform decisions: tooling, standards, architecture
- Write change notices, code reviews, and documentation
Required Qualifications:
- 4–6 years of hands-on engineering experience in a production environment, spanning both data engineering and application/backend development
- Strong dbt experience — you've built and maintained a real dbt project, not just run through a tutorial
- Deep SQL fluency and comfort with structured/relational databases (PostgreSQL preferred; Snowflake, Redshift, BigQuery all translate)
- Demonstrated experience designing and operating data pipelines (orchestration, dependencies, retries, observability)
- Demonstrated experience building applications, APIs, or services that non-engineers actually use in production
- Working proficiency with AI coding tools (Claude Code, Codex, Cursor, or equivalent) as part of your day-to-day engineering practice — you can speak to what you've built with them and where they break down
- Python proficiency for pipeline logic, transformations, and glue code
- Version control (Git), code review, and CI/CD hygiene
- AWS experience (RDS/Aurora, Lambda, S3, IAM)
- Experience with dbt-osmosis, dbt docs, or similar metadata tooling
- Frontend/UI experience for internal tooling (any modern framework)
- Exposure to sports, entertainment, ticketing, or CRM data models
- Background integrating vendor APIs (Ticketmaster, Salesforce, Adobe, etc.)
- Comfort with light DevOps (Terraform, GitHub Actions)
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.
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