Bridging The Gap Between Engineers & Analysts

Bridging The Gap Between Engineers & Analysts

Every team that takes Data seriously should get a DataOps person ASAP. It starts with a small team where a couple of data engineers (and database engineers) start catering to the requests of data analysts and scientists

It starts with a small team where a couple of data engineers (and database engineers) start catering to the requests of data analysts and scientists. Initially, the requests are simple. They don’t take much time to fulfil. As the team size grows, the data engineers take more and more additional work. A time comes when the data engineering team becomes a blocker for the business. How? Data engineers start by catering to requests that are ad-hoc in nature. They deviate from their main responsibilities of developing new pipelines, adding new sources of data, fixing issues with old data, modelling the data in lakes and warehouses and so on. In addition to the ad-hoc requests from the data analysts and data scientists, the data engineers also start catering to infrastructure, CI/CD, source control, container, system administration issues.

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