Principal Data Engineer

** The Gig:
** Virgin Voyages is looking for a Principal Data Engineer to set the technical direction for how we build, run, and modernize our data platform. You'll own platform architecture, pipeline design, reliability, and data quality, and you'll lead the way in making AI-assisted development the everyday standard for how our data engineers work.  This is a senior individual contributor role with no formal people management.

You'll work with a high degree of autonomy across Data Engineering, MLOps, and Data Ops, partnering with Analytics Engineering, machine learning teams, and stakeholders across the business to deliver data foundations that power both analytics and AI.  You will use tools like Claude Code, Cursor, and Codex every day, and you know how to pair them with solid engineering discipline: version control, code review, automated testing, CI/CD, and observability.

You'll help us build an end-to-end, AI-driven development pipeline and set the bar for howAI-generated code gets reviewed, tested, and shipped.
This opportunity sets sail as a fully remote role based in the United States of America. To come aboard, you must currently reside full-time in one of the following states: FL, GA, IL, NY, NC, PA, TN, and TX. Please note that this list is regularly evaluated and subject to change at any time. Occasional in-person meetings or team gatherings may take place at our Virgin Voyages Corporate Office in Plantation, FL.

** What You'll Be Up To:
*** Own and evolve the technical direction of the data engineering platform, including architecture, standards, and long-term platform evolution.
* Design and guide the development of scalable, reliable data pipelines and platform capabilities that support analytics and AI use cases.
* Lead the modernization of the data platform toward more scalable, managed solutions.
* Champion AI-assisted engineering across the team, driving daily use of tools such as Claude Code, Cursor, and Codex, and defining howAI-generated code is reviewed, tested, and deployed.
* Design and build an end-to-end, AI-driven development pipeline for the data engineering team, connecting AI agents and tools across planning, coding, testing, code review, CI/CD, and deployment.
* Define and enforce engineering standards, including version control workflows, code review practices, automated testing, and CI/CD for data pipelines and platform code.
* Build and improve observability across production data environments, including monitoring, alerting, logging, and lineage.
* Improve platform reliability, testing practices, and data quality across production systems.
* Create reusable frameworks, architecture patterns, and best practices that the wider data engineering function can build on.
* Partner with machine learning and related technical teams to support AI platform enablement and help shape MLOps practices.
* Collaborate with Data Engineering, Analytics Engineering, and stakeholders outside the core data organization to align technical solutions with business and platform needs.
* Contribute hands-on where it counts most, especially in building foundational services, frameworks, and platform capabilities.
* All other duties assigned  
** Superpowers

Required:

*** 10+ years of experience in data engineering or related data platform roles.
* Demonstrated experience designing and evolving modern data platforms, pipelines, and architecture.
* Strong SQL and Python skills.
* Hands-on, daily use ofAI coding tools such as Claude Code, Cursor, or Codex, with a track record of driving their adoption across an engineering team.
* Experience designing or building AI-driven development workflows that integrate AI agents and coding tools into testing, code review, and CI/CD.
* Solid software engineering practices:
Git-based version control, code review, and automated testing (unit, integration, and data quality tests).
* Experience building and maintaining CI/CD pipelines for data and platform code, with familiarity with Scala, Maven, and Bitbucket Pipelines.
* Strong experience implementing observability in production data environments, including monitoring, alerting, logging, and lineage.
* Proven ability to improve reliability and data quality in production data systems.
* Experience with cloud-based data platforms and managed services across a modern data stack.
* Experience establishing engineering standards, architecture patterns, and reusable technical frameworks.
* Experience enabling AI use cases through strong data engineering…