The skills you described are those of a data scientist/engineer hybrid. Have you tried clearly branding yourself as a data engineer? There’s a lot fewer of those, and the job is overlapping to the nearest understanding of a hiring manager or non tech person.
I think they want data scientist to do plain-old-data-engineer work, but not just plain-old-data-engineer work. Getting / cleaning data is part of the job description, IMHO. You can't be a data scientist and entirely reliant on others to do data cleaning, or you'll programming through Slack messages.
I've been a data person for the past year and a half and I'm very disappointed with the bewildering array of titles out there and the rather vague meanings behind them (Data Analyst, Data Scientist, Data Engineer, ML Engineer).
It's overall hurting my ability to build my personal brand and seek roles that are a fit for my existing skillset and aspirations.
What exactly does 'ML Engineer' communicate to employers in terms of baseline skills? Is the role closer to that of a data engineer or an analyst?
Yeah it’s not really what you should be hiring a data scientist to do. I’m of the opinion that if you don’t have a data engineer, you probably don’t need a data scientist. And not knowing who you need for a job causes a lot of confusion in interviews.
There's nothing aspiring about what you wrote. I think you're fine calling yourself a data engineer if those are the types of challenges you've been solving.
Don't sell yourself short or select yourself out of an opportunity (within reason). That's someone else's job!
One thing I would take issue with that was mentioned in the article is that I don't think 'data analyst' is a very good title for getting a next job. I was a 'data analyst' (though it was really a software engineering position) for a year and a half and got very little interest in my resume until I got 2 more years of software engineering experience and changed the job title on LinkedIn to be Software Engineer (Data Analyst).
Recruiters rely on keywords extensively, and hiring managers do, too, to an extent. "Data analyst" is not a keyword that opens doors. Use "software engineer" or "data scientist" if they are applicable.
I think I read the sentence "working with a DWH as a data engineer/analyst/scientist is like software engineering while ignoring all good software engineering practices" back then on HN. And it resonates with my personal experience of 18 years in the field of data analytics / science / engineering. Every time I try to introduce / follow at least some of the good practices (like documentation, unit/integration testing etc.) there is a major push-back from my peers and higher ups. On the other hand I managed to build up from scratch the whole data science and analytics infrastructure at my current employer on my own as the only data guy. I have defined one (yes, 1) object and had to throw once my own solution (it's not even a "library" but a dumb list of geo-coordinates) for a task where I din't found a suitable python package within 10 minutes of googling. I am practically gluing stuff together and everyone is pretty happy with that.
tl;dr: "ETL with Spark" and co. is not software engineering. It's the modern name for "writing PL/SQL on Oracle". Being good at it doesn't qualify you as a software engineer. On the other hand being good at software engineering doesn't qualify you as a "data guy".
I think the "Data Scientist" job title is overloaded–I see several clusters of skills being useful, and in my ideal world they would have similar but slightly different job titles:
–Medium Stats/ML, medium Engineering ("Data Scientist" or "Data Engineer")
–High Engineering on very large datasets, low/medium Stats/ML ("Data Engineer" or "Backend Engineer")
–High Analysis, medium Stats/ML, low Engineering ("Analyst")
–High traditional Stats, High Analysis, low ML/Engineering ("Statistician")
–High ML, medium Stats, medium Analysis ("Data Scientist")
–High ML, medium Engineering ("Machine Learning Engineer")
> But then don't call yourself Data Scientist because that puts you into the same low-barrier camp as all the others. From my own experience it's a clear resume red flag: Almost anyone that market themselves as primarily a "Data Scientist" has little technical skills.
How do you imagine the specifics here? Let's say that you have the technical skills, and come across and meaningful, technically challenging job that you like and the job title is data scientist - do you not take the job because of the title? Or do you lie about what your title is in the future?
I was under the impression that the Data Engineer role is just the market reaction to too many Data Scientists being produced without having the necessary Programming skills to self enable their day to day work.
reply