Lead Data Engineer

The Lead Data Engineer is responsible for designing, building, and governing a reliable data warehouse architecture that unifies key business data across ticketing, loyalty, and F&B systems. This role owns critical data engineering standards, identity resolution, Azure environment governance, and production‑grade pipeline development while partnering with business and technical stakeholders to deliver trusted data products that support analytics, operations, and decision‑making.

• Design and build a governed data warehouse architecture that unifies ticketing, loyalty, and F&B data into a single trusted source of truth
• Own identity resolution end‑to‑end, including defining the entity framework, setting coverage standards, and expanding capabilities as new source systems are onboarded
• Govern the Azure data environment, including monitoring, alerting, cost controls, and consolidation into a single reliable warehouse
• Serve as the internal technical counterpart to the implementation partner by auditing the existing platform, evaluating architectural decisions, and driving toward full internal ownership
• Translate complex data architecture decisions for non‑technical business partners and collaborate with analytics and operations teams to deliver data products that drive business decisions
• Mentor engineers and help build a culture of technical rigor while remaining hands‑on as a builder

• 6–9 years of hands‑on data engineering experience designing and building production‑grade systems
• Academic background in a quantitative or technical field such as physics, mathematics, computer science, engineering, or a related field
• Production‑grade pipeline development experience in Python and advanced SQL, with strong attention to performance, observability, and maintainability
• Production experience with dbt, including project structure, robust test coverage, and development of reliable transformation layers that support downstream reporting and analytics
• Production experience with Airflow or equivalent tools, such as Dagster or Prefect, including DAG design, dependency management, and failure handling at scale
• Deep cloud data warehouse experience;
  Azure preferred, with strong AWS or GCP experience transferable
• Experience building or enforcing medallion architecture in a production environment
• Experience inheriting or auditing an externally managed platform, including reviewing unfamiliar codebases and independently evaluating architectural decisions
• Experience owning engineering standards, including code review, pipeline governance, and documentation practices
• AI‑native mindset, with experience using AI‑assisted tools as a productivity multiplier