March 31, 2024, 7 p.m. | Tobias Macey

Data Engineering Podcast www.dataengineeringpodcast.com

Summary


Working with data is a complicated process, with numerous chances for something to go wrong. Identifying and accounting for those errors is a critical piece of building trust in the organization that your data is accurate and up to date. While there are numerous products available to provide that visibility, they all have different technologies and workflows that they focus on. To bring observability to dbt projects the team at Elementary embedded themselves into the workflow. In this episode …

accounting anomaly anomaly detection building data dbt detection elementary errors observability organization process products projects something summary trust up to date

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Developer AI Senior Staff Engineer, Machine Learning

@ Google | Sunnyvale, CA, USA; New York City, USA

Engineer* Cloud & Data Operations (f/m/d)

@ SICK Sensor Intelligence | Waldkirch (bei Freiburg), DE, 79183