## Metadata - Author: [[zach-quinn|Zach Quinn]] - Full Title:: Measuring Impact as a Data Engineer - Category:: #🗞️Articles - Document Tags:: [[Measuring a data team impact|Measuring A Data Team Impact]] - URL:: https://medium.com/pipeline-a-data-engineering-resource/measuring-impact-as-a-data-engineer-ff9546963121 - Finished date:: [[2023-04-04]] ## Highlights > For data engineering, a discipline whose product is precise and tangible, it’s surprisingly difficult to gauge your work’s impact. ([View Highlight](https://read.readwise.io/read/01gx6spt09t524pge0mx3dw2zx)) > when it comes to measuring output and impact, data engineers need to examine some more unconventional metrics. ([View Highlight](https://read.readwise.io/read/01gx6sq58ssks626ztb3cdc0d3)) > For data teams serving internal organizational stakeholders, there is one metric you should live and die by. > Customer satisfaction metrics. ([View Highlight](https://read.readwise.io/read/01gx6tp3znaf29cs965y35n5yz)) > Cost savings is perhaps the easiest metric to measure because cloud service providers like Google [literally provide pricing break downs](https://cloud.google.com/billing/docs/reference/rest). ([View Highlight](https://read.readwise.io/read/01gx6tse2yv893pjzsxrghdrfz))