Data Engineer (dbt + Snowflake)

Our client — a world leader in entertainment and live event experiences — is building a new dimensional analytics infrastructure to support commercial strategy, pricing, and revenue optimization across a global ticketing & hospitality business.

You’ll play a key role in transforming complex Snowflake source data into production-ready BI models using dbt, ensuring accuracy, scalability, and business impact. This is a hands-on role involving deep data validation, cross-system reconciliation, and collaboration with Analytics and Data Platform teams.

Key Responsibilities

• Design and build dimensional data models (facts & dimensions) within Snowflake using dbt.
• Translate raw “Landing” data (Bronze layer) into high-quality Analytics models (Silver layer) for downstream self-service BI use.
• Implement complex business logic, validation rules, and handle data quality issues.
• Conduct peer reviews and adhere to established Git and linting standards.
• Document models for analyst consumption in the Gold layer.

Effort distribution:
20% setup/design • 30% model building • 40% validation/iteration • 10% documentation/handoff

What You’ll Work On

• Building and optimizing dbt transformations and Snowflake schemas.
• Debugging and refining transformation logic for business accuracy.
• Collaborating via GitHub with the Data Platform team for code reviews and approvals.

• Ensuring high code quality using linting, pre-commit hooks, and consistent conventions.

   

Requirements

• 5+ years of experience building dimensional data models in production.
• Advanced SQL and dbt skills (strong understanding of materializations: views, tables, incremental).
• Proven Snowflake production experience.
• Strong knowledge of Git workflows (pull requests, reviews, branching).
• Deep focus on data validation, quality assurance, and debugging.
• Ability to work independently with minimal supervision and deliver results.
• Portfolio required: GitHub or equivalent showcasing dbt/data modeling work.

Nice to Have

• Experience in e-commerce, ticketing, or other high-volume transactional businesses.
• Knowledge of cross-system reconciliation and data consistency validation.
• Familiarity with BI tools (Tableau, Omni Analytics, Looker).
• Availability for partial overlap with US Central Time zone.

Important Notes

• You’ll not build ingestion pipelines or BI dashboards — focus is entirely on Analytics layer modeling.
• Analysts will consume your models directly, so clarity, documentation, and structure are key.
• Expect to spend significant time on data validation and iterative refinement with stakeholders.