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Tive is a shipment-tracking platform that turns device sensor data into customer-facing analytics and AI agents. The Data QA Engineer owns data quality for the company's dbt Snowflake pipelines and designs evaluation frameworks for AI agents built on that data.
In this role, you will:
**Data Quality Strategy & Implementation**
Own the data quality strategy for Tive's Snowflake + dbt + Airflow pipeline. Decide which models get which checks based on pipeline layer and customer impact. Write and maintain comprehensive dbt tests covering uniqueness, not-null, accepted values, referential integrity, timestamp order, value ranges, and business logic. Use dbt built-ins, dbt_utils, dbt_expectations, unit tests, and custom generic tests.
**Pipeline Monitoring & Health**
Monitor data pipeline health by configuring source freshness and anomaly tests. Tune severity and thresholds so alerts are rare and actionable. Build and maintain dashboards on test results so the team can see data health at a glance.
**Development Excellence**
Raise the bar in development to increase data quality. Review pull requests for test coverage and data risk, improve CI testing, and get QA involved before models ship, not after.
**AI Agent Evaluation**
Build evaluation datasets for AI agents by working with analysts and domain experts to create golden question sets with verified answers and verified SQL, covering common questions, edge cases, and questions the agent should decline. Create agent test suites with scoring rubrics and metrics for answer accuracy, SQL correctness, tool and data-source choice, groundedness, and latency. Run the eval suite on every change to prompts, models, semantic views, or underlying dbt models, and flag regressions.
Analyze agent failures and monitor in production. Read traces of multi-step runs to find out why an agent picked the wrong table, filter, or tool, and route the fix to the right owner. Track live quality over time and report it in terms the product and leadership teams can act on.
**Requirements:**
- 4+ years in data QA, analytics engineering, or data engineering with a quality focus
- Strong SQL: can read complex dbt models, work out 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 understanding 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
- Working understanding of how LLMs and agents behave, including non-determinism, hallucination, tool use, and how to measure systems whose output isn't deterministic
- Comfort with Git, pull request reviews, and CI
- Clear writing: can turn a data problem into a ticket an engineer can act on, and explain quality results to non-technical stakeholders
**Nice to have:**
- Elementary, dbt_utils, dbt_expectations, or Snowflake data metric functions
- LLM evaluation tools such as LangSmith, Braintrust, MLflow, DeepEval, or Snowflake Cortex Analyst evaluations
- Experience evaluating text-to-SQL or analytics agents, or building semantic layers
- Airflow and building BI dashboards (Sigma or similar)
- Using AI coding assistants to speed up QA work, such as model review, test generation, and failure triage
- Experience with IoT, sensor, or logistics data
