Analytics Engineer

• Design and implement facts and dimensions in dbt using staging, intermediate, core, and mart layers
• Model slowly changing dimensions and mixed-grain snapshot sources
• Make defensible grain and materialization decisions
• Migrate legacy reports and business-critical views onto the new Core model
• Reconcile report outputs line-for-line for trusted cutovers
• Use AI coding tools for model development, test writing, and dbt/GitHub workflows
• Review, correct, and own the correctness, performance, and style of AI-assisted work
• Write dbt generic, singular, and unit tests
• Establish contracts on data-out models and treat failing CI checks as release blockers
• Integrate and document diverse source systems and their data-quality quirks
• Curate mart models and metric definitions for governed self-service and agentic AI
• Add metadata such as certification, PII level, and known issues
• Follow small-PR, shared-style-guide, version-control, and documentation practices