Strong Middle/Senior Data Engineer (вакансія неактивна)

Our customer is a world leader in entertainment. You will build dimensional analytics infrastructure (dbt + Snowflake) for a high-volume live events ticketing & hospitality business. Transform existing Snowflake source data into production-ready BI models for commercial strategy, pricing, and revenue optimization.

Work schedule till 20:00 (UA).

Requirements:

• 5+ years building dimensional data models in production (facts and dimensions)
• Expert SQL + dbt with deep understanding of materialization strategies (views, tables, incremental)
• Snowflake production experience
• Git workflows: pull requests, code review, peer review collaboration
• Code quality adherence: linting, pre-commit hooks, following team conventions
• Strong data validation and quality assurance skills
• Experience debugging and correcting transformation logic
• Can deliver independently with minimal supervision while collaborating effectively with Analytics/Data Platform teams
• Portfolio required: GitHub or similar showing dimensional modeling/dbt work
• At least Upper Intermediate English levelPreferred:
  • E-commerce/ticketing/transactional business experience
  • Experience with cross-system data reconciliation
  • BI platform integration knowledge (Tableau, Omni Analytics, Looker)

Responsibilities:

• Expected Effort Distribution: 20% setup/design > 30% initial model building > 40% validation/iteration > 10% documentation/handoff
• Scope Boundaries: All source data exists in Snowflake Landing layer (Bronze). You will build the Analytics layer (Silver) dimensional models — facts and dimensions. Analysts will create their own Marts (Gold layer) using Omni Analytics for self-service BI, so your dimensional models — especially dimensions — must be production-ready, well-documented, and high quality for direct downstream consumption. You will not build data ingestion pipelines, orchestration workflows, or final BI dashboards.
• Expect significant time on validation work: fixing data quality issues, implementing complex business rules, and iterating based on stakeholder feedback to ensure accuracy.
• Code Review Process: All code pushed to GitHub for peer review and approval by Data Platform team before merging. Must adhere to established linting standards, pre-commit hooks, and data platform conventions.
• Strong collaboration and communication with Data Platform team required throughout implementation.