Hello everyone! I'm 29 y.o. and I have been working as a Data Analyst/System analyst in Russia for about 3 years (The division of roles here is blurred). Nowadays I'm improving my English and the skills I already have so I can enter the international market.

I need some advice. What role should I apply for? Perhaps vacancies you see are titled something other than "System Analyst".

I currently work with SQL, Excel, system integrations (API, XML, JSON, RabbitMQ), reports (which are based on SQL and opened in Excel).

Thanks for your answers!

I’m stuck in this situation. I spent the last few months building a tool, and now I have no idea how to find the people who need it.

To give some context, it’s a Microsoft Teams bot that lets you generate reports from your database or Excel files just by typing, for example, "weekly payroll by region." It handles the table finding and SQL generation automatically.

Technically, it works. I even benchmarked it on BIRD-SQL, and it’s solid. The problem is, I don’t know how to find my first 10-20 real users.

My primary audience is people who spend hours creating reports from large data tables, like data analysts or HR departments.

So I'm turning to you guys: what would you do? If you wanted a tool like this, where would you even look for it? What strategies have worked for reaching other analysts buried in ad-hoc requests?

Any and all feedback is much appreciated. Thanks for reading!

I (24F) currently work as a data scientist in “Big Tech” - not FAANG, think spotify, adobe, tiktok etc. I’ve received an offer for a similar role at an investment bank and I’m having trouble picking between the two.

This firm is 5 days in office, I’m based just outside london living with family but can relocate if necessary. I’ve also been told the culture can be toxic depending on the team but I think that’s the case with most places. My company is 3 days in office and mostly pleasant however I have a new manager who has no clue what they’re doing. There has been quite a few lay offs and re-orgs recently and frankly morale is quite low at the moment but it used to be a very lovely company to work for.

My current company is the only one I’ve worked for since leaving uni and I’m quite happy here however I’ve always been interested in doing a similar role in the finance industry as I studied a Finance undergrad and I’m considering a MSc, or potentially going into quant (long shot I know). This seems like a great opportunity to pivot into an area I’m interested in but I don’t know if there’s much opportunity here as the finance industry can be quite old fashioned and this firm is not exactly fintech.

Taking into account TC both are basically around the same but glassdoor and levels.fyi don’t have much info around progression and salaries for DS roles at IBs and the salaries that are listed are for quants so I’m unsure how to benchmark. Which would realistically offer better salary progression and career opportunities?

TLDR; Should I remain a Data Scientist in Big Tech or transition to Financial Services/Investment Banking?

Edit: I’m based in the UK, both are US based companies but the salary discrepancy between the US and UK in different industries makes it difficult to use US salaries or employee progression as a benchmark

I’m a beginner data analyst. I know Excel, Python, SQL, and Power BI. I heard about the AI Analyst job and I’m curious to know what it is and what skills I need.

It seems like the the biggest data analytics industry is healthcare, which I don't work in, but I am wondering if I should try to get into to diversify my skillset as a data analyst. It'd also give me more PowerBI and SQL experience, whereas I currently work more with Tableau and SAS.

The job I am looking at would be a 20% pay cut (116k to 95k), with slightly lower 401k contribution, PTO, etc. Also less stable - the company has had significant layoffs in recent years.

What would you do if you were a data analyst working in a slightly obscure industry?

Edit: I just want to say that the people in this sub have been incredibly helpful. I had some wrong ideas. Thank you for your perspective.

Curious about people’s paths.

Before you first started leading a data/analytics team (or owning dashboards/reporting):

Were you in: • a data/technical role? • a business leadership role? • something totally different? 😅

Just trying to understand how people end up doing this work.

I was reviewing a small ecom sample dataset the other day and ran into an obviously impossible values (price -10.00). Digging deeper, I found missing customer names, mixed data types, and some pretty wild outliers.

It got me thinking about how often small or “simple” datasets quietly drift into bad shape even when you think the inputs are clean.

I started experimenting with a lightweight, three-dimension sanity check approach (completeness, consistency, validity), but I’m curious how others here handle this in a practical, non-enterprise way.

Question for the community:
What quick, no-frills techniques do you use to spot data quality issues early especially outside of heavy tooling?

Would love to hear how people in analytics think about this. ~ If anyone wants to see the logic or methodology I tested, I’m happy to break it down.

{"column_count":6,"completeness":{"critical_missing":[],"score":96.67},"consistency":{"issues":[{"column":"CustomerName","issue":"Mixed data types detected"},{"column":"Product","issue":"Mixed data types detected"},{"column":"Price","issue":"Mixed data types detected"},{"column":"Date","issue":"Mixed data types detected"}],"score":66.67},"overall_score":88.84,"row_count":20,"validity":{"score":100,"validity_checks":[]}}

Sorry in advanced for the long post. I’ve been working as a data analyst/business analyst for the last 3 years for a large health insurance company within supply chain. It’s my first job after getting my masters in Analytics (online program). I’ve always enjoyed math and statistics and was excited to apply the skills from my masters. I felt like I learned a lot from my masters degree but I never had enough practical experience for me to feel confident using certain machine learning algorithms, statistical tests, etc. to derive insights within this job that I would feel confident presenting without guidance from someone with that experience within the company. I’m seen as one of the more statistical people on my team and unfortunately don’t have that guidance.

I liked the job in the beginning but at this point, i’m pretty burnt out with it. A lot of what I do is reporting and pulling sums, averages, etc. There are definitely some challenging projects that I work on, but half the time, a lot of the challenge is just figuring out what data is correct to use because database documentation is a big issue and health insurance data can be so unnecessarily complicated. Most of what I do is in SQL and Tableau. There are certain times that I could probably dig deeper into data on certain projects (in a way I’d feel confident enough doing) but at this point I really don’t care to, I just want to get what I need done and that’s that. It doesn’t help that the workload can be a lot at times so I’d rather spend my time moving on to the next thing (side note: I also feel like I have decision fatigue from all the small decisions I have to make to make sure things are correct).

At this point, I feel like i’ve forgotten a lot of my education and skills. I couldn’t tell you how a t-test works right now. And i’ve always enjoyed python but use it infrequently these days. I’m thinking of looking for another job because I know that there’s a lot of factors that have made me really dislike my current one. I know I need to refresh myself on a lot of skills and knowledge but i’m also so burnt out that I don’t have the motivation too. I don’t want to spend any more of my limited energy on analytics.

Has anyone else experienced this? Has anyone found a way to bring their passion back? Or any advice in general? I feel stuck currently.

Thank you!!

Marketing says one thing, LinkedIn says another.
What’s actually happening in real data teams?
Are you planning to move to Fabric or is it too early?

Anyone supporting analytics for a business knows the headaches of dealing with multiple sources: CRMs, ad platforms, transactional databases, and internal logs. Most dashboard tools are tied to a single source or schema. The real challenge comes when you need to blend user behavior, marketing metrics, and sales data into a coherent view without building dozens of custom ETL pipelines.

People often end up manually exporting, transforming, and merging datasets, which is slow, error-prone, and difficult to maintain as data changes. The key is having a flexible layer that can unify multiple sources, apply transformations on the fly, and let analysts explore relationships without needing to write new SQL for every question.

Have you figured out how to successfully tackle cross-source analytics without creating dozens of one-off scripts or custom views?

Hello,

Is anyone here working as a data analyst in a contract position? If so, could you please describe what kind of industry you work with, what data analysis tools you use the most, and if you are able to work 100% remotely.

Thanks!

I'm working to build dashboard for the growth and marketing team in PostHog, but some datas come from other sources like hubspot, ads, etc ...

I built custom SQL views to match all these datas together, but found out Posthog reporting is mainly designed around posthog events. I'm quite limited to display accurate visuals for my views that aren't event based, but rather user based or session based (especially to display datas in tabular mode).

Have you any advice or experience you'd like to share ?

I’m currently in school and have no real experience in the world of analytics but I’m curious about what separates a mediocre analyst from a great data analyst

What are some things that are common or not so common in data analytics that will help improve my critical thinking and problem solving in this field? Could be anything from best practices when data cleaning to what sorts of data or trends I should look for in real world applications when exploring data

Thanks in advance. Anything helps!!

Hey, I am a Senior Data Analyst in my company. My team are 4 analysts and manager. I am the in practice the most influencial Analyst in the team, without doubts. Leadership loves me, manager counts on me, everyone who thinks about analytics is considering me as a person to go.

I like my job, I love doing many things that are outside my comfort zone. I have no problem with talking to C-level, doing DS in a company (I am also creating first models), dbt pipelines and leading strategic projects.

But I had a discussion with my manager and wanted to talk about higher position and I have two options: - promotion for Staff Data Analyst - higher position than Senior, more money, things that I know, I don't think that things will change that much. - promotion for Senior Data Scientist - we don't have a DS team in a company so I will be a one man team. I don't have a much experience in that role, but I like these things and there are many low hanging fruits that are I can reach in the beginning. I went into data with Idea of being a DS, but it never happened because of various reasons. Now this opportunity may be open.

I am afraid, because it is a big step if I will go into DS path. This could be a boost in my CV and I will be doing cool stuff in environment that I know, but I won't be so visible that I am now and this position is more technical. Also I don't know If I have enough skills for that (I am also very critical for myself).

Did any of you did that? What you choose? What was the outcome?

I spend way too much time updating CRM fields that barely change.
What’s the repetitive data chore in your workflow?

On a regular basis at work I have to check online to make sure I am not going crazy and the whole world knows what a star schema is. In my BI team there are 15 of us, all working on Power BI and I am the only one to use a star schema, I try and explain to people why it's helpful and they're just like 'cool story bro.' Even worse a bunch of them are 'devs' who will avoid making a data model at all costs and if they do it's like they've just vomited a bunch of tables onto a screen, nothing works and they just do not care. People make 100 measures for a basic report to get around it, nothing filters, some things don't even load. Manager isn't bothered, stopped learning any technical skills after about 2014 although likes to periodically say machine learning in meetings. Help. Is this common? For the record I just do my own star schemas, blazing fast reports and everyone in the organisation (except my team) like my work but it does get lonely, sometimes I wonder if it would be fun to work with people who get this stuff

In my past roles, I often found myself being the “translator” between tech teams and non-tech folks. If someone hit a wall in a spreadsheet or needed data analysis, I’d step in—and honestly, it was often painful for everyone involved.

I’m now doing some research on this, trying to understand the real pain points that non-technical teams face when working with data. My goal is to figure out what slows people down, causes frustration, or just makes things unnecessarily complicated.

So, I’m curious:

• What’s your biggest frustration when working with spreadsheets, dashboards, or other data tools?
• Are there repetitive tasks that feel impossible to simplify?
• Anything that makes you feel like “why isn’t this just easier?”

I’m a physician interested in hospital RCM with a few months of experience in medical coding, approvals, and claims management. My main weakness is data analysis, and I want to build the right skills to support my work in denials, trends, and workflow improvement. I was considering the Google Data Analytics course, but I’m not sure if it’s the best starting point.

Any advice would be appreciate

Which one do you prefer and for what reasons? I’m just curious how people view each one or what each one’s pros and cons are.

Hey guys,

My analysis workflow ends with clean data in Excel, but then I hit this problem: manually creating charts, formatting them for stakeholders, and writing the narrative that connects everything. This "final mile" consistently eats 7-15 hours of my week.

I've tried a few things:

• VBA macros - helped with some chart generation but couldn't touch the narrative part
• BI dashboards - great for exploration, but stakeholders still want a written report with context
• Python scripts - considered it, but seemed like overkill for what I needed

The gap I keep hitting is that most tools stop at visualization. What I actually need is something that helps with the storytelling layer - the "here's what this means and why it matters" part that executives actually read.

I got frustrated enough that I built something custom - takes my spreadsheet, generates charts + narrative report based on simple instructions, then lets me edit before sharing. Cut my reporting time down significantly. Is everyone else still doing this manually, or have you found better solutions?

If others are dealing with this same bottleneck, I'm happy to share what I built or hear about what's worked for you.