The role asks for someone to take charge of how data is modeled, moved and reported across a company that runs on device, ad and computer vision data. A strong candidate has built data models and production pipelines before, writes SQL and Python hands on, and can turn vague questions from engineers and business people into tables and dashboards they trust. Correctness and explicit definitions matter more than polish here.

How it fits the AI era

The company's product depends on computer vision watching live TV, so the data work sits close to AI output. Much of the pipeline building and monitoring can be automated, but deciding what the data should mean, setting shared definitions and judging whether numbers are right stay human. The role also lays groundwork for measuring how well the models perform.

How it builds AI experience





Working here means handling detection output from vision models and building the evaluation and ground truth data those models are checked against. That gives direct exposure to how AI systems are measured in production. Someone preparing for this could practice building evaluation datasets and pipelines for model outputs before applying.

Worth showing

- Production ETL pipelines
- Expert SQL and Python
- Warehouse experience

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Taiv

An AI system that watches the live TV feed in bars and restaurants and swaps ad breaks for venue content and targeted ads, with the venue sharing the revenue.

📌 Senior Data Engineer (Winnipeg)
🏢 present of AI
📍 Winnipeg