Data Engineering jobs

Data engineering at an AI company means the pipelines are the product's bloodstream: ingestion at web scale, deduplication, filtering, and the lineage tracking that answers what the model actually trained on. The classic warehouse work hasn't gone anywhere either — someone still builds the tables the business runs on.

Spark and dbt remain the workhorses, with Airflow or Dagster orchestrating and Ray increasingly handling the ML-adjacent jobs. Postings here mention petabytes without exaggerating. Demand has only grown as training data became a competitive weapon, and this is one of the most remote-friendly, salary-transparent corners of the board.