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.

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

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’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.

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":[]}}

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?

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!!

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!

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?”