In my previous writings, I droned and dribbled about how strange the workplace is. This is particularly true for an office environment, and doubly so for the cursed few of us who work in Tech. It seems that themes of incompetent leadership, buzzword salads and extreme variation in skill level in regard to position title are commonplace across a myriad of disciplines; software engineering, data engineering, data science and cybersecurity to name a few.
So, in the past I was contemplating and recalling my adventures through tech and the various disparities I experienced in regard to the following:
- Position Title
- Job Description
- Interview Questions
- What you actually end up doing at work
I often found huge discrepancies in what the workplace expected of me and also what I expected I would be doing, with issues stemming from the various permutations of those specific dot points just mentioned. I neglected to reflect on why these things may happen and also failed to provide any possible solutions for our lizard-skinned recruitment interviewers to consider while looking for talented staff (and perhaps, even retaining them!).
Let’s kick it off by looking at some reasons as to how people end up in jobs and also the reasons as to why some jobs exist at all in the first place.
The flow of time itself is convoluted; with heroes centuries old phasing in and out
Over the ages, a long, long time ago a man stood alone. He hunched over his desk in the wretched dark and contemplated distributions and numerics at length, wondering why, why?! Why is it so difficult to understand how distributions of numbers change depending on the context?! He eventually wrote a piece of work titled Statistical Methods for Research Workers and the mathematical populace celebrated. We’ll ignore the fact that this particular man had extremely strong opinions on things such as eugenics and race, but we can all respect the works he contributed to the field of modern statistics.
What that tangent has to do with what I’m saying largely comes down to how modern society values different skill sets over time (groundbreaking thoughts, truly). Originally, statistics was viewed as a boring and dry field that was relegated as only useful to mathematicians in the basement of universities tending to their dark magics of distribution proofing and statistical moments.
Fast forward to the explosion of being able to record every single interaction a person has with a piece of technology, discovering that data is actually quite useful if used correctly alongside a significant remarketing ploy going from ‘statistics’ (SNORE, boring, lame, cringe) to ‘data science’ (WOA, sexy! Amazing! Do you want a side of AI with your dashboard?) We find ourselves moving from the Machine God’s graces and closer to the Chaos-aligned, Agile shamanistic rituals where we move spiritual cards around a virtual ouija board until it looks like we did our jobs.
Much like AI, I think organisations need to consider more as to why they are needing to hire someone in the first place as opposed to jumping on the bandwagon immediately.
Who are you, and what do you want?!
Across my adventures in this field, I’ve had many names. I can safely say I’ve officially had the following applied to my person across various organisations and institutions:
- Data Analyst
- Data Scientist
- Data Engineer
- Senior Data Engineer
- Cloud Platform Engineer
- Data Visualisation Specialist
What I’ve often found is that I don’t normally end up doing what I’m supposed to be doing. I have a fairly forgiving persona, so I considered ‘not doing what I’m supposed to’ at a threshold of about ‘if I’m not doing what I’m supposed to for 60% of the time, then something is wrong’.
Now, the reasons one can end up in similar situations have a myriad of explanations. One of the ones that I’ve identified that are a huge culprit are to do with organisational bloat and bureaucracy. That is to say, the relationship between an organisation's size and its ‘slowness’ of movement are linearly correlated. In plain English, the larger an organisation gets, the worse off they are for doing work at a reasonable rate. You simply get too big for your own good, it gets so difficult to manage every single pocket of the organisation, you lose visibility due to the bureaucrats who inevitably make their way in and you’ve just been informed the entire Eastern front has fallen to Hannibal Barcid riding his elephants over a mountain.
Anyway, what I’ve noticed is that a team or department is provided with funding of some kind to hire some more people after some growth. This may include operational expenditure or capital expenditure and how you spend these two types of money will influence your relationship with the person who provides you with these funds - i.e. the Finance Department, who’s sole reason to exist is to prevent you from spending money.
Let’s say you have a certain amount of money to hire someone new. You’ve talked to your team and discovered you really need someone who knows how to migrate data from point A to point B from a legacy system into a new, shiny system. You’ve been provided with a certain operational expenditure and you are only allowed to hire someone that is referred to as a ‘Data Analyst’.
Team Lead ‘Capitalist Craige’ (that’s ‘Craig’ with an ‘e’, thanks very much) knows he can weasel his way through this by changing the position description with ambiguous wording to secretly end up hiring someone who has more engineering skills than analytics skills. What usually ends up happening is a bunch of analysts end up applying, they go through 100s of applications and say ‘you’re not quite what we’re looking for’ and they end up settling on someone who just needs a job to pay rent and is willing to just say ‘yes’ to everything on the job description. Everyone loses in this situation and it was all thanks to the whims of HR and Finance having zero ‘wiggle room’ on both position title and salary ranges. Way to go team.
As an addendum, the other way people end up in these positions are simply due to significant organisational restructuring and playing musical chairs with people’s livelihoods. This is particularly apparent when a new executive is installed amongst the silicone columns of the C-suite where they need to prove to everyone that their rule is final and absolute and resistance will be met with being turned into Servitor for their transgressions of asking ‘wait, why are we restructuring, exactly?’.
Anyway, one of the best examples I saw of this was a few years ago where the organisation I was working in underwent a very significant restructure and assimilation of the various data groups into a single blob. One of the people I worked with was originally in a position that was effectively a ‘Senior Business Analyst’. The organisation didn’t have enough budget or wiggle room to place this person into a directly equivalent role and instead landed them into a ‘Senior Data Engineer’ role. Keep in mind, this person had never used Git before, didn’t know Python was a programming language and was now running weekly release meetings in front of 8 other engineers and was somewhat responsible for the health and maintenance of the data platform. Way to go team.
Leon : Tortoise? What's that?
Holden : [irritated by Leon's interruptions] You know what a turtle is?
Leon : Of course!
So, the main issue with interviews is you can’t really get a solid gauge of how talented someone is, truly. The best thing you can hope for is an inference of what they’re capable of. Of course, there will be variation in the disparity between those two things which makes interviewing not only difficult, but extremely time consuming as well. Most places I’ve worked, I’ve never heard someone say ‘yes, we are working on this at a reasonable pace and we have time to complete the project properly’. It is almost exclusively the opposite and everything is rushed at all times, giving rise to a new ‘normal’ workplace where rushing is expected. This applies to finding new candidates as well.
In an ideal world, it would be grand if both the interviewer and interviewee could be 100% transparent with what they needed from one another. However, I’m going fully on record and I’ll admit I’ve told some pretty spectacular lies in interviews. My favourite one so far is simply changing any references to ‘Postgres’ on my CV to ‘Snowflake’ and seeing the recruiters' eyes turn into dollar signs. It’s SQL all the way down, people.
Having said that, I’ve also had employers just straight up lie to me as to the reason I was hired in the first place which I’ve covered previously. Now, we all find ourselves in this strange arms race of lying to each other where both sides need to lie harder than the other in order to win this strange game that is unnecessarily convoluted to begin with. The game being, you do work and you produce something for someone who needs it then you get paid for your time. It should be that simple, but it never is.
What exactly would you say you do here? - Well, look! I already told you!
So, with the explanations I’ve provided above you can probably see how you end up in this weird situation to begin with. It seems to be a mix of everyone lying about everything, not being up front with what needs to be done combined with people not actually knowing what needs to be done because they’re in the same situation you’re in while also just following whatever the newest trend is to justify a new position title to HR and also stringent criteria set forth by other departments in an organisation.
I’ve worked jobs before where I don’t end up doing exactly what I’m supposed to and had a good time. I’ve also worked at places where I don’t end up doing exactly what I’m supposed to and had an extraordinarily bad time as well! It’s been a mixed bag, truly and I’m still glad I have all those experiences (the good and the bad!) because overall I’ve managed to learn over time and I’m better for it. However, to be honest I’m not even sure if you can fix the above aside from simply being up front and more accepting that either side won’t be able to provide you with 100% of what you need or expect from them.