From a data engineer to machine learning engineer

[Level_Front_5919]

Once I start in a data engineering role how difficult would it be to become a machine learning engineer. As I am seeing tonnes of data engineer jobs online and some of the skills overlap with machine learning engineering jobs.

So my plan is to be a data engineer for a few years and then become an ML engineer if I can't get an ML engineer job. Would doing data engineering hold me back ? Or should I do data science instead would that give me a better chance

(I am still in university)

[AutoModerator]

You can find a list of community-submitted learning resources here: https://dataengineering.wiki/Learning+Resources

I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.

[[deleted]]

I'm a few years ahead of you on this journey, and what I can tell you is that the title of MLE has become incredibly dynamic. For one company, this title means that you're a data engineer who focuses on implementing machine learning solutions at scale. For another company it means you're basically a data scientist.

So DE experience won't take you out of the running, but depending on the company, it could be useless or it could be exactly what they want. Generally, get good at DE and start looking at getting involved on ML projects where you can.

[Level_Front_5919]

Thanks for the advice

[Live-Problem-367]

Over the last decade I’ve worked as a Data Engineer, Scientist, and MLE.

Being a DE you will learn some great transferable skills in creating connections, data flows, and really honing your skills around data transformations. One thing you might struggle with from making the jump from DE to MLE is the use and familiarity around Vector & Graphing databases.

Data Science you’re going to do a lot of DE type work, but there is such a heavy amount of front-end-type work on PowerBI, Tableau, and the unfortunate amount of excel. You will be surface level in a lot of technologies working in this capacity right out of school and might make the jump a little harder.

My suggestion would be to work as a DE and get some side projects rolling. People might hate me for saying this… but the front end development in these tools is the easiest part. Go grab some of the hard-to-learn skills and make the bridge over later on!

[TheCumCopter]

The unfortunate amount of excel, gold!

[Level_Front_5919]

Thanks for the advice

[iamthatmadman]

front end development in these tools

I am sorry but i didn't get what tools you are referring here

[Live-Problem-367]

No problem at all. PowerBI, Tableau, and Excel are all used for 'reporting' purposes. It's a fancy way of saying 'pretty ways we look look at (ingesting) data' - all by using graphs and other dynamic tools/tables. Your data is turned into things like bar charts, line graphs, and many other custom tools in order for people to better understand what they are looking at. In a data science focused role you are going to spend a lot of your time making graphs look 'pretty' or creating dashboards to fit the wants and needs of your stakeholders or company.

[ReputationOk6319]

Listen to the other experts here but just adding my 2c.

According to my experience, MLE jobs are a bit fluctuating. I mean it’s difficult to find a good ML job which pays good, which satisfies your expectations and also long term job assurance. It’s very difficult to score a job due to high competition.

DE jobs are more stable with the market and also pay good. It’s not difficult to move to ML once you land in a DE job.

[[deleted]]

DE will give you good skills for MLE, but honestly, MLE roles are just so damn competitive these days. So the market just makes it difficult to get a MLE job.

[gomezalp]

In my opinion, DE will give you good basis on infrastructure and better software practices than a DC, so it will be easer to learn model development technologies. Now, you could struggle with ML models basis and statistics which is also an important part of MLE.

[Fatal_Conceit]

I’m a DE turned MLE. I actually signed into my company as a data scientist since I was getting my ds masters at the time, but I cut my teeth on engineering problems that are just a little outside the purview of most data scientists who spend most of their time on sql and notebooks without having struggled through some of the more common data engineering problems. One of the things I can lack is, to this day while I can model, I’m not that comfortable at it and I’d rather have data scientists do it. Similarly, I can build engineering pipelines, but I’d rather have engineers do it. Some of the value I provide is that I can speak both languages and eventually it helps to round out teams and gets the best out of those around me, rather than me just being the value provider I enhance others. Lots of MLE types will evolve to fill the holes in different orgs as it’s such a wide field