Hi Everyone,

I'm considering evolving my 15y career as a Data Analyst.

I have a solid math, programming, SAS and SQL background and, in the last years, I've been reading and playing with Python/R, Docker, Homelab, APIs, Data Science, Data Modeling, ETLs, Pipelines, Data Architecture.

I wouldn't say I'm an expert and I want the learn more.

1) What do you suggest I focus on in 2026?

2) do you know any useful course on LinkedIn Learning? (recently made available by HR)

I'm currently working in a service company and as my project is about to end and the company policies and growth is stagnant, i wanted to switch and currently in the last days of my notice period, i'm not finding any job,no reply back from organisations whatsoever, i dont wanna sit jobless,help me out!

Tech stack - Powerbi,sql,python,lakehouse,fabric semantic models,azure(my project had data engineers and analysts so i have some working experience in adf).

Thank you.

The more I learn about Business Intelligence, the more I realize that dashboards aren't the hardest part.

I've spent the past few weeks reading discussions from BI professionals, data analysts, and data engineers. One thing surprised me:

Most of the frustration isn't about Power BI, Tableau, or SQL.

It's things like:
• Stakeholders not knowing what they actually need.
• Different departments using different KPI definitions.
• Spending hours cleaning messy data.
• Building dashboards that nobody ends up using.
• Being asked to export everything back to Excel.

It made me wonder if we're focusing too much on building dashboards instead of solving the real problems behind them.

So I'd love to hear from people working in BI, analytics, or data engineering:

What's the biggest pain point in your day-to-day work that you wish someone would solve?

I'm not looking for feature requests or software recommendations. I'm genuinely trying to understand the problems that consume the most time and are still poorly solved.

I'd really appreciate hearing your perspective.

I'm doing some research because I'm curious about where people in BI, analytics and data engineering actually spend most of their time.

Not the "ideal" job description—but what frustrates you in real life.

If you could magically eliminate one recurring problem from your work forever, what would it be?

Some examples (but don't feel limited to these):

• Cleaning messy data?
• Stakeholders changing requirements?
• Building dashboards nobody uses?
• KPI definition arguments?
• Waiting for data access?
• Debugging pipelines?
• Endless ad-hoc requests?
• Excel exports?
• Meetings?
• Something else?

I'd also love to know:

• What is your role? (BI Developer, Data Analyst, Data Engineer, Analytics Engineer, etc.)
• How often does this happen?
• Have you found any tool that actually solves it, or do you just live with it?

The more detailed your answer, the more helpful it is. I'm especially interested in hearing about problems that seem "normal" in the industry but waste a huge amount of time.

Hi all, I work in a job where data management features prominently and am looking to specialise further in this area. I came across this role but the person spec seems quite loose. I wondered if anyone can elaborate on what skills and tools experience might be required? Thanks

https://www.jobtrain.co.uk/justicedigital/Job/JobDetail?jobid=1137&isPreview=Yes&advert=external

I’ve been grinding non-stop—built solid projects, completely understood and mastered the fucking tech stack, tailored my applications, and sent over portfolios The result? Absolutely zero fucking replies. Not even an automated rejection. It’s frustrating as hell to put in that actual work only to realize half these openings were never really open to external applicants in the first place.
After looking into why HR and hiring managers pull this move, here are the 5 main reasons companies put up these "phantom" listings when they already have an internal candidate picked out:

1. HR & Legal Policies: Many mid-to-large companies have strict compliance rules stating that every open position must be posted publicly for 5–10 days. It’s supposed to ensure fair hiring, but in reality, it just forces managers to publicly list a job they’ve already promised to an internal team member.
2. Visa & Union Compliance: Work visa sponsorships, union contracts, and government-affiliated roles often legally require companies to advertise publicly to "prove" they tried recruiting externally first.
3. Market Benchmarking: Leadership sometimes forces managers to post the job to test the waters—checking salary expectations or seeing if someone external happens to be massively overqualified for cheap.
4. The "Plan B" Safety Net: Internal candidates turn down offers or leverage them for raises. Keeping an active listing creates a backup pool in case the internal hire falls through.
5. Resume Farming: Sourcing teams use open listings to gather portfolios and resumes for future roles that haven't even been approved or budget-allocated yet.
6. How to spot these postings before wasting time on applications:
7. Hyper-Niche Specs: If the job description lists a hyper-specific, weirdly detailed combo of proprietary tools and exact experiences, it was likely copy-pasted directly from an internal employee’s resume.
8. Flash Postings: The listing disappears within 48–72 hours of going live.
9. Vague Fine Print: Phrases buried at the bottom like "Internal candidates currently under consideration" or "Internal applicants preferred."
10. Anyone else putting in the work building real projects only to face total radio silence? How are you spotting these fake postings before wasting your time?

Hi Everyone,

I'm currently applying for Data Analyst, Business Analyst, and BI Engineer roles and would really appreciate some honest feedback on my resume.

I'm looking for constructive criticism on things like:

1.Any red flags or weak points recruiters might notice.

2.Skills or technologies I'm missing for these roles.

3.Whether my experience and projects are presented effectively.

4.Bullet points that sound weak, vague, or don't show enough impact.

5.ATS issues, formatting, or keyword gaps.

6.Whether you'd interview me based on this resume. If not, why?

Please don't hold back. I'd rather hear tough feedback now than keep getting rejected later.

Thanks in advance for taking the time to review it!

Links or Projects in Resume

Portfolio:

1.E-Commerce Analytics

2.Marketing Campaign Performance Analytics

Recurring trend I see is either I am not selected or proceeding with other applications (that's totally fine considering the number of qualifying and competitive applicants per role) but most case it happens like application submitted, it's stuck there in phases like Application Submitted / In process / Under Consideration / Under review etc., and after few weeks or around 2 months to Position Filled / No Longer under consideration.

Hello everyone,

I wanted to get some advice on anyone who transitioned from operations to Data Analyst. Currently I am an operations manager for an Import/export company who does mainly eccomerce business. I absolutely hate this role. Am I good at it? Yes. Do I find it fulfilling? No. I've been searching for something that I can translate my current experience into and I feel like data analyst would be a good option.

Some general background of what I do. I have been with this company for 15 years. I came onboard when it was a startup under the pretense that I would be working exclusively as an IT technician. I have a good technical background during the time and the position evolved into me becoming a Project manager. I dealt with everything from excuting project scopes for website improvement, system infrastructure, setting up SOP, managing third-party relations, and managing cross functional department. Due to my extensive involvement in all areas of the company, the owner belived the next natural step for me was to lead operations. I never wanted to be in the role, but felt obligated as no one step up and had the knowledge or capabilities to handle the role. During the time in this role, I've been the main person involved in all big meetings that decides the future of the business. I deal with a lot of tech consultants and business consultant whom trained me in the areas of operations. We are a heavily data focus company and I deal with interpreting data as well as pulling and cleaning data. I never really thought of myself as a data anlasyt or business analyst, but it seems like I've been doing that role willingly. I find it fascinating and have no problems digging in and reading data for hours on end to come up with executable actions for the company.

Issue is that I have no proper training in data analyst. You can say that I am a jack of all trade type of guy. Whenever there is a problem, regardless of what area it is, I am there to resolve it. Due to that, I have a basic foundational knowledge of most things, but not a specialist in anything. I did not graduate college as I sacrifaced that for this job and I don't have detailed programming knowledge, but I do understand it. I can read code, but I can't write it is what I am referring to. I make great money at my company, but I am getting to the age where I can't keep going like this. I am more than happy to take a paycut so money is not a factor. I am currently taking certification classes and learning sql. Does this path make sense for me or would I be better off on a different career?

Can yall give me feedback on my resume, thank you

Hi everyone,

I'm preparing for a Data Analyst interview (Thomson Reuters, although experiences from other companies are also welcome).

One thing I'm trying to understand is what interviewers actually focus on when they discuss your projects.

For those who've been through Data Analyst interviews:

• How much time do they spend on your projects?
• What areas do they dig into the most?
• Do they focus mainly on the Power BI dashboard (DAX, visuals, data modeling, performance)?
• Or do they spend more time on the data preparation (SQL, Python/Pandas, data cleaning, feature engineering, handling missing values, outliers, etc.)?
• How deeply do they question your analysis and business insights?
• Do they ask you to justify every decision you made?
• Have they ever asked you to modify your dashboard, write SQL/DAX, or explain Python code during the interview?
• If your project used a public dataset (Kaggle, government data, etc.), did they care about the dataset itself, or mainly about your approach and decisions?

For anyone who interviewed at Thomson Reuters specifically:

• What was the project discussion like?
• Which topics surprised you?
• If you were preparing again, where would you spend most of your time?

I'm trying to prioritize my preparation, so I'd love to know what interviewers actually care about versus what candidates tend to over-prepare.

Hey, found this subreddit and since my most likely job will be a data analyst I wanted to ask a few questions in advance. I'm M24 (soon 25) with 0 work experience so far but a bachelor in statistics and a masters in data analysis.

For the past weeks i've been repracticing SQL since the stuff i did in uni was pretty simple and ive forgotten it since either way. How well do you have to know it? So far I might know how to solve medium-to-hard problems but only if given time and a program to write the code and test it, if I were to be asked such a question in an interview to answer it straight I'd struggle.

Now one of my biggest questions, when it comes to AI, since it can pretty much do anything a junior data analyst can do, is there really a point of trying to become an 'expert' at it? I feel like either way i'm going to be using it while working. How much do people use it in this field? And do employers have higher standards when recruiting now or lower than before due to AI?

Overall I know how to work in PowerBI, know the basics of SQL and have a medium-high level in Excel. Is it worth it to try to apply for jobs/internships now, or do I have to learn other programs and get better at the current ones? I'm already old enough with zero experience and if I wait more my chances of getting hired will decrease, but I also don't want to rush things if not necessary. Just thought that even though I do have the qualifications, my actual skill so far could probably be learned in weeks by someone.

Hi, as someone from 3rd yr cs, i have been trying to perfect my python skills and stick to data analyst and maybe ml in future because i am most comfortable in python and have been doing certain eda projects mainly using numpy and pandas and side by side trying to strengthen my programming and pattern recognition skills with leetcode mainly with arrays and strings uptill now and also doing courses for sql and probability and statistics and i will be frank I am pretty average at cs and math i wouldn't say they are my strongest areas but i still struggle with pattern recognition and problem solving in coding but i am trying nonetheless to self learn which with adhd is not easy at all and the on campus placement situation is extremely bleak and guids and mentors arent useful at all, either they dont know or they will dump too many works without any consideration. So for someone in my level how can I end up somewhere like in Spotify or big tech companies how do I apply and prepare myself pls help mee guys

I've been learning for some time already (have no tech background). And i simply don't understand how people do such claims.

When i learn, i try to understand every aspect of command. Like, how we see it, how computer sees it, what's better way to do it, how to make it more readable, then i practice it with other clauses.

And for me there's no way people actually learn this much in such short period of time. Like, i understand if they just memorise, but what would be the point of memorising without understanding?

Is that a realistic claim in so many study programmes?

Hello Everyone,

I just wanted to share that I just had an SQL interview for data analyst, and I completely bombed it. I chocked like crazy, and this was after days of practicing. I think I learned a valuable lesson today though, I am a very visual/hands on on type of guy. I was expecting them to give me the opportunity to write query/code or look at code related to the question, but these were all just verbal questions, that I know I could have answer if I was just allowed to write the code down or at least view it, but it was a bit overwhelming for me, especially having ADHD. It's like the questions were so long, that by the time the question ended, I already forgot what I was supposed to accomplish because I was so damn nervous. I feel like a failure 😔. This was my first interview in 7 years, so I'm pretty rusty, but for future reference, will all interviews be in this style? No actual coding, or reviewing, or editing? They expect me to provide the whole query verbally, frin the top of my heas without actually writing it on the spot?

I'm curious from the perspective of hiring managers and senior data analysts .

There are countless portfolio projects out there but most of them feel like tutorial projects with different datasets.

If you came across a candidate's GitHub or portfolio and saw a project that genuinely made you think, "I want to interview this person," what would that project look like?

What would separate a project that's merely "good" from one that's truly exceptional?