Principal Data Engineer

Virgin Voyages•Remote - FL, TN

2d•Remote

About The Position

Virgin Voyages is seeking a Principal Data Engineer to set the technical direction for building, operating, and modernizing its data platform. This senior individual contributor role has no formal people-management responsibilities and offers substantial autonomy across Data Engineering, MLOps, and DataOps. The role partners with Analytics Engineering, machine learning teams, and business stakeholders to deliver data foundations for analytics and AI. The Principal Data Engineer owns platform architecture, pipeline design, reliability, and data quality, and champions AI-assisted development as a standard engineering practice. The position uses tools such as Claude Code, Cursor, and Codex and establishes disciplined processes for reviewing, testing, and deploying AI-generated code. It also involves building an end-to-end AI-driven development pipeline that connects AI agents and tools across planning, coding, testing, review, CI/CD, and deployment. This is a fully remote position for someone residing full-time in the United States in one of the following states: Florida, Georgia, Illinois, New York, North Carolina, Pennsylvania, Tennessee, or Texas. Occasional in-person meetings or team gatherings may take place at the Virgin Voyages Corporate Office in Plantation, Florida.

Requirements

10 or more 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 of AI coding tools such as Claude Code, Cursor, or Codex, with a track record of driving 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, including Git-based version control, code review, and automated unit, integration, and data quality testing.
Experience building and maintaining CI/CD pipelines for data and platform code; 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 foundations.
Proven ability to operate independently and lead technical direction with a high degree of autonomy.
Must currently reside full-time in Florida, Georgia, Illinois, New York, North Carolina, Pennsylvania, Tennessee, or Texas.

Nice To Haves

Experience with GCP, including BigQuery.
Experience with dbt or other modern data transformation frameworks.
Ops or DevOps knowledge, including familiarity with Cloud Run and Kubernetes.
Exposure to MLOps concepts and collaboration with machine learning engineers.
Experience building AI agents and agentic workflows, including Model Context Protocol (MCP) servers that connect AI tools to data platforms and internal systems.
Familiarity with LLM application patterns such as RAG, embeddings, and vector search, as well as evaluating and monitoring AI systems in production.
Experience with DataOps practices and modern platform operating models.
Experience in travel, hospitality, consumer platforms, or recommendations and personalization use cases.
Strong stakeholder communication and cross-functional collaboration skills.

Responsibilities

Own and evolve the technical direction of the data engineering platform, including architecture, standards, and long-term platform evolution.
Design and guide development of scalable, reliable data pipelines and platform capabilities for analytics and AI use cases.
Lead modernization of the data platform toward more scalable, managed solutions.
Champion daily AI-assisted engineering practices and define how AI-generated code is reviewed, tested, and deployed.
Design and build an end-to-end, AI-driven development pipeline integrating 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 in 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 for the wider data engineering function.
Partner with machine learning and related technical teams to support AI platform enablement and shape MLOps practices.
Collaborate with Data Engineering, Analytics Engineering, and stakeholders across the business to align technical solutions with business and platform needs.
Contribute hands-on to foundational services, frameworks, and platform capabilities.
Perform other duties as assigned.