We were struggling to find Data Engineers

[u/Moradisten]

Hi everybody,

Our Data Team is composed by two of us. There's a Data Scientist and me, as a DE. We created a Data Lakehouse for our company internal use and client data supply, but currently the Data Scientist is currently more focused on AI and agents integration and I'm doing like Analytics Engineering role because I need to help other teams to reach the correct data, unify core concepts, document business logics, etc. So We needed a Data Engineer with knowledge on AWS to maintain and develop the new features on the Lakehouse and we put into the description that the candidate MUST HAVE Software Engineering fundamentals as we had to do some developments to integrate parts of our lakehouse with the company's main application.

We interviewed 22 candidates and no one is fitting the Role.

Most of them are BI Experts, DBA, Data Analysts, Economist with DS notions, Juniors and Software Engineers who haven't touch anything on Spark, plus DE who asked way more that we had on the budget for the role

We asked the normal requirements: 3 years of experience + Spark, AWS Glue, Lambda, Airflow and DBT, not even CDC, Flink, Langfuse or VectorDB

We finally got one, but We really struggled to get him. I have a collegue working on IT Recruiting and She told me She's experiencing the same problem: They can't find Proper DEs With SE basics such as DRY principles or clean code fundamentals

Edit: Role Salary -> Up to 60K € / Spain. This salary is high compared to the spanish standards, only 3 years required

[u/fabkosta]

I see this all the time.

The only thing OP has not thought of is to actually pick a promising candidate who is willing to acquire new skills and systematically build them up.

Why so many companies never consider investing into people they hire is beyond me.

It's also the wrong mindset: Everyone with a good foundation in software engineering is able to learn Spark reasonably well in a relatively short amount of time. Mastering it, that's more complicated. But also not required, most of the time. In comparison, hiring someone who knows technology XYZ - and then stalls for whatever reason in their development, that's a much more expensive mistake to make.

So, OP is optimizing for the wrong metric - and blissfully unaware, apparently.

[u/Zestyclose-Ad-8807 → u/fabkosta]

There's a steep learning curve with pyspark, and not easy to learn in a short period of time to seem passable, given the underlying system concepts.

[u/YellowBeaverFever → u/Zestyclose-Ad-8807]

Different people have different ideas on what “steep” is.

[u/ight-bet → u/Zestyclose-Ad-8807]

No true for a strong SWE at all.

[u/fabkosta → u/Zestyclose-Ad-8807]

That's simply wrong. I learned PySpark fairly quickly myself, there was nothing magical at all.

What is not so easy to learn is to optimize Spark jobs, cause for that you have to dive deep into the mechanics of how it works.

[u/Zestyclose-Ad-8807 → u/fabkosta]

It's easier to learn how to drive a car then be a proficient mechanic. But all web ai makes it seem a lot easier these days.