Analytics Engineer

The role centers on turning raw data from products, sales and vendors into clean, reliable datasets that colleagues can trust. A strong candidate brings solid SQL and Python, hands‑on dbt experience, and a good grasp of dimensional modeling on a Databricks lakehouse. Just as important is the ability to run pipelines in production, explain trade-offs to non-technical people, and work independently while raising problems early.

How it fits the AI era

AI coding assistants speed up writing, testing and documenting SQL, dbt and Python, so more time can go to design and quality. Automated documentation, data checks and self-serve answers for colleagues are also part of the picture. What stays human is judging whether numbers are right, deciding how data should be modeled around real business questions, and earning stakeholders' trust.

How it builds AI experience

This job offers practical experience using AI assistants on real data engineering work with a review step built in, plus a chance to try AI for documentation and quality checks on a platform serving local marketing data for many brands. To build this ahead of time, practice using an AI assistant on a small dbt project, then review and test everything it produces. Trying an AI-driven way to answer questions over a clean dataset is also a good exercise.

Worth showing

• dbt models in production

• Dimensional modeling for analytics

• Databricks or Airflow pipelines

• Data quality and monitoring

• Clear Metabase reporting

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 Uberall

Uberall is a multi-location marketing platform that helps enterprise brands manage their local digital presence, per the company.