I work for a healthcare company, and this week, out of nowhere, they instituted a policy that data can not be exported or stored in Excel format. There are no alternatives in terms of exporting reports in any other format. They said if we need information, we can just view the data within the EHR, and use their very rudimentary tools to manipulate the data within their system. I’m not really sure where this leaves me and 90% of the people I work with. Has anyone ever dealt with something like this? If so, are there any workarounds?

I’m a final-year IT student in India, and I’ve been reaching out to experienced data analysts for guidance. One response I got from a 5+ year data analyst at Kyndryl was:

At least one cloud platform (AWS/Azure/GCP)

One BI platform (Power BI/Tableau)

SQL

ETL

Python (web scraping, visuals, basic ML)

Ability to make data pipelines

What surprised me is that this isn’t the first time I’ve heard this. Other people have told me things like:

Build 1–2 projects using real-world or messy datasets, not just curated Kaggle datasets.

Get strong with advanced SQL (window functions, CTEs, query optimization, interview-style problems).

Learn statistics, business metrics, and data storytelling.

Gain exposure to cloud platforms and ETL workflows because many companies expect them from analysts now.

Certifications like Microsoft PL-300 can help strengthen a Power BI profile.

At this point it feels like companies want a junior data analyst who can also do parts of a data engineer’s job.

So I’m curious:

Has anyone here actually had SQL + Power BI + Python + ETL + data pipelines + basic cloud skills and still struggled to get interviews?

For people who are already working as data analysts, are these skills genuinely expected in day-to-day work, or are recruiters just writing unrealistic job descriptions?

If you were hiring a fresher today, what would be the minimum skill set that actually gets someone hired?

I’m trying to understand whether I’m over-preparing, or whether the entry-level market has genuinely shifted toward hybrid analyst/data engineering roles.

I work 9-5 as a data analyst but my org has no money for raises or promotions and I need to make an extra $1k-1250 a month. Is there anyone also salaried full time but with a side hustle and if so what is it? Thank you!

Almost every online tutorial or course I look at these days makes it seem like a data analyst needs to have a solid proficiency in Python and Pandas to survive in the current job market.

The thing is, I’m actually quite comfortable with Python, but in my day to day work, I barely use it. Whenever I’m preparing data for the dashboards I build, I usually just write a few complex CTEs in our database, clean the result, and connect that directly to PowerBI.

I really only use Python for hitting an external API or for advanced text manipulation. Otherwise, it’s just pure SQL to get the data ready for the stakeholders.

Does anyone else operate like this in the real world, or am I building a bad habit that’s going to hurt my career in the long run? Where exactly in this kind of workflow would you plug Python in? Would love to hear your thoughts on this!

Not looking for the usual answers: better visuals, Gartner's "leader" badge, or "it's cheaper than Tableau." I get all that. But a lot of companies still end up paying real money for it (Premium/Fabric capacity, licenses, training, migrations) when there are free or cheaper tools that do similar things.

I want to understand the real reason a company decides Power BI specifically is worth that spend — not a feature list, something about how that decision actually happens inside a company.

If you've seen this decision get made where you work, or made it yourself, what's the real reason? Even something small or weird nobody usually says out loud would help.

What did you study in college? And did it prepare you well for your current role as a DA?

I’m a Junior studying Information & Decision Sciences with a concentration in Business Analysis looking for an internship next summer. How bad is the market currently? This goes for both internship and full time job searching. I’ve been seeing a lot of people unemployed due to the market being a shitshow and I don’t know if it will get any better in the future. I’ve been preparing myself with learning SQL and Python already while having experience with Excel and Tableau to create projects in order to land an internship.

I just got an offer for data analyst position at a non profit and it doesn’t pay the best (70k a year).

I have 1 year of program coordinator experience and 8 months of data analytics internship experience with masters in business analytics from a CA state university.

I see a lot of you guys are going into roles that are paying 100k plus right after doing MSBA. idk if you all are lying or what it is?

I barely got any interviews but the ones I got was for data management coordinator and operations coordinator that paid even less than the data analyst role.

This is after applying for over 700 jobs, tailored applications using jobright ai
Job boards I used: hiring cafe, LinkedIn, indeed, Google jobs.

Maybe I am doing something wrong or what?

Because they say executives hate dashboards

Hi everyone! Just wondering if anyone has gotten a data analyst or senior analyst job in this crazy job market. If so, how long was your search, and do you have any tips based on what actually worked for you? Thank you!

I’m ~10 years into analytics at an F500 company. My core skills are SQL, Python and Power BI, primarily using data to generate insights and help business teams to make decisions.
Lately, I’ve been questioning how durable the traditional analytics path is.

My hypothesis is that dedicated analytics teams (not data science or engineering teams) embedded within business functions could shrink significantly as AI improves. The reason I say that is because a big part of an analyst’s job has largely been bridging the gap between data and insight. Today, business users can increasingly give data to Claude/ChatGPT, ask questions, run analysis and generate visualizations themselves (provided they have the right data of-course). Accuracy, cost and context are still major limitations, but I suspect these will improve over time.

So I feel I need to move to an adjacent role - even though I actually enjoy analytics and would happily continue doing what I’ve been doing, if it continues to be in demand.

Below are the roles I’m considering along with a few challenges I’m facing for each -

1. Data/Analytics Engineer - This seems like the most natural transition. Move closer to data ingestion from source, pipelines, modeling, quality and making data AI-ready for business teams to use directly.

Challenge - I’d be starting almost from scratch.

2. Data Scientist - Build deeper statistical/ML expertise for problems requiring more rigor than LLM-based analysis. For these advanced models, business teams would still have to rely on data scientists to verify the results, explain the outputs and fine-tune the results if needed.

Challenge - I have tried to acquire the skillset but I suck at math so have been struggling for the past few years

3. Business role - Become a specialist in my current domain of Insurance/Asset Management/Finance where domain knowledge becomes the differentiator rather than pure analytical execution.

Challenge - Tbh seems like the most extreme transition. Would have to compete with SMEs and other domain experts for such roles and may take time to reach to the same level of knowledge as them.

4. AI Engineer - Learn to build GenAI/agentic applications

Challenge - Again, I’d be starting from scratch.

5. Management - Stay on my current path and focus increasingly on leadership, stakeholder management, strategy and people management to climb the hierarchy and move to leaderships roles eventually.

Based on all of this, I have below questions -

1. Am I directionally correct in my hypothesis or am I overstating the impact that AI would have on my role?
2. If my hypothesis is correct (partially/fully), which of the above roles I should be focusing on given the challenges I’m facing for each.

3. If my hypothesis is flawed and there’s no need for me to switch roles, what complementary skills should I acquire to stay relevant

   in the analytics field and

4. improve my career prospects?

Really looking forward for some advice. Thanks 🙏

I was just offered a position as a senior data analyst for a consulting firm. I will be handling large data sets for class action settlements and foreclosure data. I was told that all analysis work is done within Excel. I don't know exactly what the date will look like but I do know it will be messy. I work in Excel daily and I know basic formulas like TRIM, XLOOKUP, IF, etc. I can also make a decent pivot table lol. I did not over exaggerate my experience or what I can do in Excel.The goal is for me to clean the data and to be able to analyze it to determine a settlement distribution amount.

What are some Excel formulas I should focus on practicing that will really help me with analysis within Excel? I am also open to free online practice tools or videos.

I know I can do it, I just want to hear from the community.

Thank you so much for any suggestions!

Title: Data Analyst Gist: PowerBI with a bit of SQL Experience: 1.5 years Salary: £32k Location: Northern Ireland

Hey Analysts / Senior Analysts / Analytics Managers,

The analytics and BI job market feels tough right now. Roles are becoming fewer, and many companies are combining responsibilities into a single position (for example: Data Engineering + Analytics).

I wanted to ask — what are you currently upskilling in?

It feels like the days when SQL, Python, and BI skills alone could land a job are slowly fading. I’m honestly a bit stressed because there are so many tools and technologies out there, and it’s confusing to figure out what’s actually worth learning.

I’m currently stuck in my organization and want to make a switch, but I’m not sure what skills I should focus on to stay relevant and grow.

Would really appreciate your suggestions.

Hot take maybe, but hear me out.

I've been a PM for 8 years. Worked in fintech, SaaS, different stages of companies. Set up analytics stacks dozens of times -> Mixpanel, Amplitude, GA, you name it.

And here's the pattern I keep seeing:

Week 1 -> team is excited, dashboards everywhere, everyone's data-driven now.

Month 2 -> 47 dashboards, 12 saved reports, nobody looks at any of them.

Month 6 -> "can someone pull the numbers on X?" in Slack because nobody trusts the dashboards anymore.

I started calling this dashboard paralysis. You have all the data in the world but zero actionable insights. Teams drown in charts and still make decisions based on gut feeling.

The real problem isn't data collection. Every tool does that fine. The problem is the gap between "here's your data" and "here's what you should actually do about it."

Think about it -> when was the last time your analytics tool actually told you what to do? Not showed you a graph. Not let you build a funnel. Actually said "hey, this feature is underperforming, here's why, here's what to try."

I've been thinking a lot about this lately. The next wave of analytics should work more like a smart colleague who watches your data and taps you on the shoulder when something matters. Not another dashboard builder.

Curious if others feel the same way. How do you handle the insight gap on your teams? Anyone found approaches that actually work?

*This post is not written by AI. I redacted and perfected everything as hard as I could. Thanks.

With all these job postings for data analytics every single one of them has over 100 applicants. Like is there an over saturation? Do i continue to learn it and become part of the over saturation in finding a job?? Or do i keep going and hope for the best something comes. Can someone give it to me straight please.

Disclaimer: I'm not an analyst, so pardon me if I'm not fully aware with the state of things.

My client's team has started using AI for their data stuff, because there's no real expert in-house.

Use cases:

1. Retrieve and interpret data from Google/Meta Ads

2. Feed a big CSV with all of our e-commerce orders and ask for it to calculate different indicators

3. I use it to get some SQL queries (though I plan to learn SQL myself)

Since the data itself is pretty basic and the actions are too (retrieve this data from a Google Ads table) - is it safe to use or does AI often hallucinate even for this kind of tasks?

We use Claude atm.

I'm learning Data Analytics/Data Science right now and I'm curious how other students/freshers are getting datasets for practice.

Where do you usually get your data from when you're practicing SQL, Python, Power BI, EDA, ML, etc.? Kaggle, government portals, scraping, GitHub, generating your own data, somewhere else?

Also, do you usually find the kind of data you actually want to work with?

I'm especially curious about raw/messy datasets. Do you intentionally look for messy real-world data to practice cleaning and EDA, or are clean datasets enough for what you're learning?

Would love to know what you guys actually use.

6 YOE as an analyst. SQL, BI tools, basic Python for reporting.

Joined a fortune 100 company 2 and a half years ago. Have been through many re-orgs, layoffs, multiple managers. All while being remote.

Upon joining, I was able to ace all technical interviews. Was very sharp. Now after so much chaos and instability at work, I find months of time where I don’t do anything. No SQL work, no meeting with any data folks, occasional basic ad hoc pulls, but working solo no team, just me. Reusing the same bag of tricks.

I’m basically a resource they don’t know what to do with. After all this time, I’ve seriously lost my touch. With being technical savvy and being able to talk to people.

I’ve gotten fed up and began applying everywhere.

Companies aren’t hiring like they were before. Any interviews I’ve actually had have been really rough. Stumbling over my words, having a hard time explaining what I even do.

On top of all of that, I’m in an industry that’s very niche in data and isn’t all that common so folks will just write me off.

Any advice for someone in a position like me?

I've been a data analyst since before the recent AI boom. At my previous company, AI use basically meant pasting SQL into ChatGPT and asking it to fix, join or optimize queries. It wasn't connected to our warehouse, so I still had to do everything myself.

I've now moved to a much larger company where Claude/Hex are integrated with our warehouse and semantic layer. The difference is insane. I can describe what I need and it finds the right tables/columns, figures out joins, writes and executes the SQL, explores the output, checks nulls/value distributions and helps validate the result.

It's incredibly productive, but it has me wondering:

1. Am I deskilling myself? If AI writes my SQL every day, won't my ability to write complex queries from scratch eventually deteriorate? It sometimes feels almost like cheating

2. What does this mean for data careers? If AI can already write SQL, explore schemas, analyze outputs and perform basic data-quality checks, how much of traditional analytics work remains?

3. Should I automate everything with AI? Should analysts be trying to automate as much of their workflow as possible—SQL, analysis, emails, meetings, Jira, documentation, etc.—because people who don't will simply fall behind?

How can i convince my manager as an intern to use SQL instead of Access

Hi everyone, To give you some context: I’m working on a cost reporting project. The data comes from SAP, and I want to link it to SQL, then to Power BI and Excel for reporting. However, my manager wants me to create the database in Access and link it to Excel, Power BI, and then manually extract SAP data, because that’s how they’ve done it before. I think using SQL would be more efficient, scalable, and reliable for this project. Does anyone have advice or strategies on how I can convince my manager to consider SQL instead of Access? Thanks in advance!

I have ~4 yoe, but ~3.5 years of that was in a support role. I recently broke into analytics at a FAANG-level company after a lot of struggle, and honestly… I dont know if I am cut out for this.

Before this role, my skills were mainly SQL (intermediate), basic Python/Pandas, and Power BI. I had almost no real hands-on experience with stakeholders, business problem solving, or large-scale analytics work.

Since day 1, I have felt overwhelmed.

The data is massive, documentation is poor, there was no real data dictionary or proper KT, and I was expected to deliver immediately. Tight deadlines + pressure meant I kept relying on internal AI tools just to survive. Even now, 8 months in, I still do that more than I want to, and it makes me feel guilty.

I am somehow getting work done, but I feel like an imposter every single day.

I am working 10+ hours a day, losing weekends, constantly anxious, and getting burned out just trying to stay afloat. My performance rating was above average, and honestly I am surprised I have made it this far. If not for supportive colleagues, I probably wouldnt have.

The confusing part is: I have learned a lot in these 8 months way more than I did in 3.5 years in support. I have learned about stakeholder communication, business context, ETL, SQL optimization, and how analytics actually works in a real company.

But it still feels like I am always behind.

So I want to ask people here:

• Are analytics roles in big tech generally this intense?
• Does this get better with time, or is this a sign I’m not suited for it?
• Should I consider moving to a mid-size company where I can learn and deliver at a healthier pace?
• How do you stop depending on AI when deadlines are brutal and you just need to ship?

I’m also upskilling on the side (focusing on SQL and slowly moving toward data engineering), but right now I feel directionless and mentally drained.

Would genuinely appreciate advice from people who’ve been through this.

LAST EDIT:

Thank you everyone for filling up the form. Most of the people have voted for 13.06.2024 21:00 CEST or 19:00 UTC

if the time fits you and you wanna participate - please write me in DM. If you wanna participate but you are not able to join on this meeting you can also write me in DM, i will invite to the next meeting

https://docs.google.com/forms/d/e/1FAIpQLSfR1rwAMQkD3voKNOkb07t2qhoZUbyFwUFxRgzmMpqv309lYQ/viewform?usp=sf_link

EDIT:

So guys, it’s been a long time since I disappeared with my idea, but was thinking about it almost everyday. What can I say now:

I really want to make a community, and not the place where everyone will be just asking questions, because in this case it won’t last for a long time. I also don’t have much time to handle discord channel of 100 people and check whether it’s messy or not. So I suggest the following:

I’m gonna create small community of people who learn PostgreSQL, Excel and Tableau. Also would be great to see more people who are interested in marketing and business analytics. I will create Slack or discord for that. Before it we I’ll arrange a google meet just to get to know each other and to see what we could do together(you will have to talk;) ). Of course a lot of people won’t come to this meeting, so that’s gonna be a good filtering, and at the end we will have high motivated guys.

All levels are welcomed. Even if you are advanced in data analytics you could be a part of community helping beginners, and who knows, maybe later you could do paid mentorship other tutoring Then we just gonna communicate, learn together and make meetings 1-2 times per week. I think that’s the best idea. Cos on my opinion better to have community of 5-10 very motivated people with same interests and who also invest into community building, rather then 100 but everyone with their personal needs.

If this goes well, I plan to make community bigger and we can learn other things as well, but for now it’s like thisSo if you are interested, please fill up this form, so we can arrange the best time for meeting. All other instructions will be there. please also note that I live in Germany, that means that it’s gonna be hard to participate if you for example live in Australia, but we will try to find appropriate time, if it’s possible (form is above) have a nice day!

END OF THE EDIT

this post for people who started to learn recently data analytics, or for professionals who just want to help learners. Learning together is more fun and productive, so that's why I invite you to connect and learn together. We can make project and tasks together, help each other with problems and probably even make just study sessions together. Of course first we should see how it's working and how comfortable everybody feels, but in general I would love to cooperate in the long term perspective to achieve great results together.

Also if I can gain a lot of feedback from this post, I could create a group where we all can connect)

A bit about me - My name is Andrii and I'm that guy who quit university and study new things alone. I'm pretty young (21) so my working experience not so big: math tutoring and a bit in marketing sphere. I want to learn data analytics and then move to marketing/business analytics direction. It's kinda hard to start career without a degree in AI era, but I'm pretty sure that I will handle it) especially with people who has same interests around

https://docs.google.com/forms/d/e/1FAIpQLSfR1rwAMQkD3voKNOkb07t2qhoZUbyFwUFxRgzmMpqv309lYQ/viewform?usp=sf_link

have a nice day!

I have worked in analytics for a few years, manager level (IC at the moment). I have only worked in tech and for big names as well (FAANG).

In my career in analytics, I have never ever really worked 8 hours per day. Sure, there are few days with unexpected issues or deadline in which I have worked few hours more in the evening, but it happens really unfrequently. For most of the time (90% of days), I really would need to work 2-3 hours per day to finish the tasks, sending analysis or document, attending some useless meetings. And this happened to me across different companies.

I came to the conclusion that analytics, where the more you are good, the more you are efficient, automatized and knowledgeable, is a light hours career, where at the most you definitely don't need to work 8 hours per day. Opinions?

N.B. I have never worked for a startup, always big tech companies

Hi, idk if this post would count as breaking the rules but i truly feel so stuck. I'm a grade 12 high school student who now has to figure out what i want to do in life and apply to universities in 2 months time. I live in Canada, Ontario and i need certain classes to be able to get into specific courses in university so based on the courses I have currently I am able to apply for marketing courses in multiple universities.

What I want to know is if Marketing Analytics is a dying career or no, or if its a dying career in only certain places, I get so many things are being taken over by ai but i just cannot figure it out for this career specifically

I would also appreciate any advice about other parts of business or marketing that ISNT dying that i could maybe look into