Why New Data Engineers Need To Watch Their Mouths

Why data engineers need to be precise with words, terminology and concepts in — and outside — the workplace.

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One of the journalism lessons that stuck with me relates to death. Not the existential crisis that accompanies thinking about one’s limited time or the inevitability of mortality. But the verb, to die.

Since western society is historically bad at discussing death, we often tiptoe around the morbid and say “passed away.” I wrote this in a story and lost points.

The comment?

“People don’t pass away. People don’t pass. People don’t join the afterlife. People die. So this is what we write.”

Despite the darkness of the subject, there’s an important lesson: Precision, especially in language, matters. This is doubly true in a discipline as complex and precise as data.

In data engineering, stakeholders really only judge our work using two criteria:

• Timeliness
• Accuracy

Put otherwise: Is the data available on-demand? Is it correct?

To achieve these standards, especially accuracy with final data, involves a lot of collaboration at all stages of the data science life cycle. Which means…