Convene Hospitality Group - Analytics Engineer

• Partner with the business. Work with Finance, Sales, Operations, and venue teams to understand their needs and turn them into models and metrics they trust. • Partner with the business. • Own ingestion for your domains. Set up and maintain Fivetran connectors, land data in bronze, and handle schema drift and load failures. • Own ingestion for your domains. • Model the business. Build dbt models that move data through bronze, silver, and gold, and shape gold around how CHG runs so people outside the data team can understand and use it. • Model the business. • Own the semantic layer. Set metrics, dimensions, and relationships so a number means the same thing in a dashboard, a search, or an agent’s answer. Tune synonyms and descriptions so a model picks the right measure, trace wrong answers to the root cause, and help us decide where each definition lives. • Own the semantic layer. • Power self-service in ThoughtSpot. Build the models and Liveboards that let teams explore data on their own, and turn the questions people actually ask into new test cases. • Power self-service in ThoughtSpot. • Own the quality of conversational analytics. Test answers the way you’d test code. Build and maintain a golden question set of real business questions paired with certified answers as your test cases, run them as a regression suite on every model or semantic change, and block releases that fail. Back your models with dbt tests and clear documentation. • Own the quality of conversational analytics. • Keep it fast and fresh. Monitor freshness, data quality, and pipeline health so problems surface to you before they reach a dashboard or an agent’s answer. Tune query and model performance, from incremental dbt models to warehouse sizing, so answers come back fast and Snowflake costs stay in check. • Keep it fast and fresh. • Work AI-native. Lead with specs, then hand agents end-to-end tasks, from exploring source data to building models, tests, and documentation. Let agents run loose in the wild (with some guardrails in place). You hold agent and human work to the same review bar, so code from Claude and code from a teammate get the same scrutiny. Share what you learn, and help set the playbook for agentic data work. • Work AI-native. • What We Look For: • What We Look For: • You have 3+ years in analytics engineering, data engineering, or BI development, building data products people rely on. • You write strong SQL and have built and maintained dbt models in production. • You’ve worked in a cloud data warehouse, ideally Snowflake. • You’ve worked with ELT ingestion tools like Fivetran and can troubleshoot source-data issues such as schema changes, late-arriving data, and API limits. • You understand data modeling: dimensional models, medallion layering, and clear reasoning about grain, slowly changing dimensions, and event lifecycles. • You’ve built dashboards or self-service data products that people use to make decisions. • You test and document your work as a habit. • You’re curious about the business. You ask what decision a request supports before you start building. • You use AI agents for real multi-step work, like writing SQL and dbt models, generating tests, and drafting documentation, and you verify what they produce. • You explain your work clearly to technical and non-technical partners. • You take ownership. You see work through, ask for help when you’re stuck, and look for ways to make things better. • You’ve evaluated LLM or natural-language analytics output, for example with golden sets or regression testing. • You’ve worked in hospitality, events, real estate, or another customer-centric, multi-location business.