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Salary: USD 120,000 - 160,000 / annual
Convene Hospitality Group (CHG) operates a global portfolio of lifestyle brands focused on creating places and experiences that bring people together. The company designs, develops, and operates hospitality-driven destinations to host gatherings of all kinds, ranging from small corporate meetings to immersive brand activations.
You will join CHG's Technology organization as an Analytics Engineer on the Data Platform team, reporting to the Engineering Manager. Your mission is to build out the foundational platform to democratize data at CHG so that a venue GM, a finance lead, and an AI agent can all ask a question and get the same trusted answer.
Analytics engineers own the full path from ingestion to visualization, including governance and observability. You will bring data in from source systems, model it through bronze, silver, and gold layers, define the semantic layer, and power visualizations, conversational analytics, and business agents that teams rely on to make decisions.
Key responsibilities include:
- Partner with Finance, Sales, Operations, and venue teams to understand their needs and turn them into models and metrics they trust.
- Set up and maintain Fivetran connectors, land data in bronze, and handle schema drift and load failures.
- Build dbt models that move data through bronze, silver, and gold layers, shaped around how CHG runs so people outside the data team can understand and use it.
- 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, trace wrong answers to root causes, and help decide where each definition lives.
- Power self-service in ThoughtSpot by building models and Liveboards that let teams explore data on their own.
- Own the quality of conversational analytics by testing answers the way you'd test code. Build and maintain a golden question set of real business questions paired with certified answers as test cases, run them as a regression suite on every model or semantic change, and block releases that fail.
- Apply governance day to day: tag sensitive data at ingestion, apply access controls such as tag-based masking and row access policies, and follow team standards so an agent only returns what the person asking is allowed to see.
- Monitor freshness, data quality, and pipeline health so problems surface before they reach a dashboard or agent's answer. Tune query and model performance and manage Snowflake costs.
- Lead with specs and hand agents end-to-end tasks, from exploring source data to building models, tests, and documentation. Hold agent and human work to the same review bar.
You will build on Snowflake, dbt, and Fivetran, using Snowflake-native capabilities like semantic views and Cortex to power ThoughtSpot and beyond.
REQUIREMENTS:
- 3+ years in analytics engineering, data engineering, or BI development, building data products people rely on.
- Strong SQL skills and experience building and maintaining dbt models in production.
- Experience working in a cloud data warehouse, ideally Snowflake.
- Experience with ELT ingestion tools like Fivetran and ability to troubleshoot source-data issues such as schema changes, late-arriving data, and API limits.
- Understanding of data modeling: dimensional models, medallion layering, and clear reasoning about grain, slowly changing dimensions, and event lifecycles.
- Experience building dashboards or self-service data products that people use to make decisions.
- Habit of testing and documenting your work.
- Curiosity about the business; you ask what decision a request supports before you start building.
- Experience using AI agents for real multi-step work, like writing SQL and dbt models, generating tests, and drafting documentation, and verifying what they produce.
- Ability to explain your work clearly to technical and non-technical partners.
- Ownership mindset: you see work through, ask for help when stuck, and look for ways to make things better.
BONUS:
- Experience evaluating LLM or natural-language analytics output, for example with golden sets or regression testing.
- Experience working in hospitality, events, real estate, or another customer-centric, multi-location business.
