At Tive , we imagine a fully-transparent world in which everything and everyone is connected. We innovate beyond what the world thought possible—so what people hold near and dear arrives on time and in full. Backed by Techstars.
About the role
Tive's shipment-tracking platform turns device sensor data into customer-facing analytics, and increasingly into AI agents that answer questions about that data. The Data QA Engineer owns whether those outputs can be trusted. In this role you will build and run data quality for our dbt Snowflake pipelines, from raw source tables to customer-facing marts. You'll also design how we evaluate and test the AI agents built on top of that data. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 4+ years in data QA, analytics engineering or data engineering with a quality focus
- Strong SQL. You can read a complex DBT model on your own, work out its grain and join risks, and write validation queries that size an issue
- Hands-on DBT experience: tests, contracts, schema.yml documentation, sources and freshness, and how incremental models fail
- Experience with a cloud data warehouse, preferably Snowflake
- Experience designing tests with judgment: knowing which checks matter for which data, and keeping alerts rare and actionable
- A working understanding of how LLMs and agents behave, including non-determinism, hallucination and tool use, and how to measure systems whose output isn't the same every time
More about this role
Tive's shipment-tracking platform turns device sensor data into customer-facing analytics, and increasingly into AI agents that answer questions about that data. The Data QA Engineer owns whether those outputs can be trusted. In this role you will build and run data quality for our dbt Snowflake pipelines, from raw source tables to customer-facing marts. You'll also design how we evaluate and test the AI agents built on top of that data.
- 4+ years in data QA, analytics engineering or data engineering with a quality focus.
- Strong SQL. You can read a complex DBT model on your own, work out its grain and join risks, and write validation queries that size an issue.
- Hands-on DBT experience: tests, contracts, schema.yml documentation, sources and freshness, and how incremental models fail.
- Experience with a cloud data warehouse, preferably Snowflake.
- Experience designing tests with judgment: knowing which checks matter for which data, and keeping alerts rare and actionable.
- A working understanding of how LLMs and agents behave, including non-determinism, hallucination and tool use, and how to measure systems whose output isn't the same every time.
- Comfort with Git, pull...
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