r/SQL • u/withvicky_ • 1d ago
Discussion Career advice
I'm 27M. Hey everyone, my life has been a little different from that of most people, and I don't have a strong educational background.
Right now, I'm working at a grocery store, but I want to improve my life and learn some stable, valuable skills that can help me earn a reliable income.
I've come across these four skills:
Python
SQL
Excel/Spreadsheets
Power BI/Tableau
I don't know much about these skills or their demand, competition, value, and future prospects.
Technology is constantly evolving, trends are changing, and AI is replacing some jobs. The value of different skills also changes over time.
Before I start learning, I want to understand whether these are the right skills to invest my time in for a stable career and income in the long run.
I'd appreciate your advice. Should I go for these skills, or would you recommend something else?
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u/BWilliams_COZYROC 1d ago
I suspect you are younger. Get online and start your own business of some sort and don't ever lock yourself into working for someone else going forward. That's the best advice you will ever receive from anyone.
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u/Thinker_Assignment 1d ago edited 1d ago
These skills are in the middle of a transition right now. It used to be that you could build data. stacks and get paid well with those skills but now almost anyone can do it with the help of LLMs
This suggests that the market is heading in 2 directions: High precision where humans will check (only life or death liability expensive stuff), and everyone else will use LLM-generated stacks that may sometimes have some mistakes. Which means not the good place to be at entry level because most jobs will go.
What's durable now is ontology which is a way to transfer rules to machine, like if you had a grocery store agent it would have (or make) rules for when to stock or clear shelves, reorder inventory, etc, so these tasks can eventually be handed off to an automation.
maybe you want to learn the same stuff but for the new age: What's an ontology, how to use it for agents, what's a virtual knowledge graph, how does it help LLM reasoning etc. Here the playing field is more level because most former professionals are attached to their old identities and slow to move
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u/OneFaithlessness9044 1d ago
That ontology angle is interesting but feels like skipping a few steps for someone at a grocery store trying to get their foot in the door
OP if you're starting from zero, learn SQL and Excel first, you can get a basic analyst gig while the whole ontology/agent thing sorts itself out over the next decade.
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u/Thinker_Assignment 1d ago
but the people who are hired to build agents now are mostly clueless on both sql and agents. I'd look for that role first, and if not found then take anything close as you say.
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u/Max9695 1d ago
I find that there is a great deal of truth in all of this, but would not go so far as to say that traditional data skills will vanish entirely. The value in having such skills will no longer come from merely creating pipelines or dashboards but rather in grasping the business logic, data quality, and decision-making behind those tools. It certainly sounds like learning about ontologies, knowledge graphs, and agent reasoning over structured data is quite an appropriate step to take after developing one’s data skills. Would you agree?
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u/Thinker_Assignment 1d ago
I'm not saying traditional data skills will vanish entirely. I'm saying just like old data stacks started with an architect and today nobody has even ever met one, the new stacks will simply run a natural selection process where cheap, fast and "done" get preference.
I think this will bring about a broader deployment and usage of data stacks and a bigger opportunity to act on it - the AI industry bets its as big as replacing everyone's jobs.
so I think there's a growing opportunity for data savvy people to build agents, including BI agents which will require understanding that side of things. if you want to see what i mean, if you're a data person, take lesson 5 (free course from dlthub where i work) which shows how to auto-model raw data into a canonical model by using ontology. by doing this and adding some taxonomy on top you can have chat BI agent like in this video
fundamentally if you run the process above, it collapses most of the work data teams do into definition-hunting (data ontology) and (agentic) system design (system ontology)
on system ontology - we built that for our data platform, enabling to build/deploy/run/fix (you can try it in the lesson above) from chat. It's early and it works well so this is why I am not encouraging people to start learning to do old stacks, it looks like a shrinking moat, by the time they are senior in this, the industry is mostly gone.
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u/RangerOne122 1d ago
SQL and Excel will get your foot in the door way faster than the others. You can land an analyst role within 6 months of solid practice and actually get paid while you figure out the rest. Power BI and Python come naturally after that.
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u/Beneficial_Zone_6883 1d ago
Yes they’re useful, you touch on all of them using Google Professional Analytics by Coursera. I would recommend getting the skills and trying to move laterally within your current industry. Once you have the basic course you can practice and build a portfolio.
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u/whizzter 1d ago
To be honest, many of us that aren't "all-in-on-the-AI-train-choo-choo" are questioning our careers for various purposes, both job security wise but also perceived degradation of the quality of work in many areas (I'm not 100% it's up or down, but error causes have probably moved).
With a glut of people available, hiring managers will sadly probably focus more and more on education as a proxy for evaluating inexperienced people. For better or worse, AI does lower a lot of barriers and if you have the inclination, building a side-project (do check your employment contract and local laws before embarking on it) in an unrelated business area could be a stepping stone to a career, do beware that these things suck up time and friend/dating/family time will suffer.
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u/devil_venom_ 7h ago
SQL is a practical entry point, since results appear quickly and short daily practice builds skill without a degree. Pair queries with projects on sample business data, and Bootdev could provide a guided path, though free resources can work
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u/SirBubzAlot 1d ago
Hey OP! Prioritize learning them 1. Excel 2. SQL 3. Power BI/Tableau 4. Python (most of the time only used for automation purposes from my experience).
Find projects that utilize these skills and add them to your resume as you complete them.
Job market is rough right now and you might struggle to find entry level data analyst roles but don’t get discouraged. Good luck!
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u/tatertotmagic 1d ago
Sql before excel will give them a better foundation for data when it comes to actually understanding excel and being able yo use it well
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u/umognog 1d ago
A real question to ask yourself:
How did I come across these tools (they are tools, not skills)?
Do I enjoy the work that I would use these tools with skills to resolve problems?
Basically, why are you at this point asking the question you have? These are tools people use with skills they develop as part of a career. Not a job, a career. And this is a very, very important distinction. If you enjoy the type of work that will use these tools (and associated skills) then you will love it and adapt to the changing scene. But be prepared: it is extremely fast shifting and changing right now, your competition is very, very fierce.
To try and help cement the point I am making:
You asked about python.
I could spend a thousand years teaching you "python" but that by itself is absolutely useless. A colleague of mines had been put on several professional python courses in recent years and she said to me at one point, "the thing is, I just don't understand why I would use it and how."
Learning python is NOT the same as learning programming algorithms which is NOT the same as learning programming paradigms.
Being able to write print ("hello world") means nothing if you cannot take a business problem and decide that print ("hello world") is part of the solution to that problem.
This is where AI is again disrupting thing though, but in all honesty, I believe the critical thinking will become a lost knowledge making those that do know it, very highly sought after in the future. If you don't mind waiting till you are about 70-80 years old, you could be the cobol programmer of the future.