r/analytics

I’ve never worked as an analyst, so I have no real picture of what the job looks like day to day.

What I’m most curious about is how much of your week goes into digging through data to answer a question or track down a problem, compared to everything else you do. I’d also love to hear about a recent problem you only caught because of the data, and how long it had been going on before anyone noticed. And from the outside, I can’t really tell how much of what you find ends up changing decisions versus getting looked at and shelved, so I’d be curious how that goes for you.

Thanks in advance to anyone who takes the time.

So two years ago I was a data analyst and then we reorganised and I became insights partner, which is the kind of title a consultant invents on a whiteboard.

Recruiter messages in the 12 months before: 14.

In the 18 months since: 1, and that one was for a job in a different country.

I only noticed this last week as my work got better, my output got more meaningful, and I became invisible at the same time. I'm now fairly sure nobody is searching insights partner because nobody outside my company knows it exists.

The obvious fix is to put "data analyst" back on, but that's not my title and guess it might get flagged in my team.

For people with invented internal titles - do you stick with it, or go for something more well known?

Hi everybody! I’m 3 years into my data analyst career and as of January 2026 I’ve been promoted to senior data analyst. I think a pretty good at my job and I actually got an award last year for my work despite this I only make ~ 80K, even though I live in a mid to high cost of living area. Most of my friends and acquaintances that work in the same or similar field as me make six figures at least
… I’m wondering what I’m doing wrong. I went to pretty good public school in my state and I feel like I know the same things that everyone else does SQL Python, platforms like databricks, data visualization tools, and etc. typical data analytics tools.

I occasionally apply for other data analytics job openings, but I never get past the initial application phase before a rejection. Acquaintances in the same field as me always talk about how they get recruiters calling them and offers for different jobs, but I’ve never experienced this. I wonder what I’m doing wrong? My undergrad degree was not in computer science or engineering, but I’ve produced enough work on my projects list to show that I’m capable of handling complex stuff. Is it really just because my undergrad degree doesn’t say computer science? I still have a STEM degree (BS).

I guess my real question is: what makes recruiters reach out to people and give them offers? The people telling me they have offers have just as many years of experience as I do.

How many offers are you guys getting a year and how many years of experience do you have? And as someone with only three years of experience, should I be expecting to get offers?

I do NOT NEED ADVICE FROM AI BOTS BUT REAL PEOPLE

i have three interviews coming up back to back which is totally unexpected. It’s for summer 2027 internships and these are the following roles: business analyst, data integration analyst, and corporate analyst.

I really want the data integration role the most because it ties into what i want to do most, data analytics. Im also okay with the business analyst role since they’re p much the same thing

This is my first ever technical interview for a company so im really nervous so do any of yall have any tips. Im a junior so these are really important opportunities to me.

I've been building an analytics studio app to visualize digital marketing data from key data sources (like GA4, GSC, Google Ads, SEMrush, Nozzle, Profound, adobe etc.). It's a drag & drop dashboard builder; built on Next.js, React 19, Gridstack and eCharts. Supabase is the database, BigQuery is the data warehouse.

Currently the app is hosted on my local computer, no github repository, no hosting, etc.

I want to host the app on Vercel because it integrates/ships relatively seamlessly. But our agency's analytics team would like to host the app on Lovable, because that's what they use for dashboard creation. I have explained that Lovable does not support Next.js and it would require substantial re-writes (possibly taking weeks or months).

Hoping some analytics pros in here could provide me with some talking points on why Vercel is the better option (beyond having to rewrite the app in lovable) - things like error logging, app/user management, data accuracy, scalability, etc. If you have any good use cases, I would love to hear them! Thanks in advance!

I’ve literally had 2 interviews but I think I am also in a rough spot as I am fairly senior (10+ years) and my compensation is extremely high for an IC in my area.

I also am a more “traditional” analyst and I do not have experience with LLMs. Luckily I still have job but it’s hell on earth with all the PIPs, layoffs and productivity tracking.

I even started to have wild thoughts that maybe I’m blacklisted or something lol. I use AI to tailor my resume but no hits. I tried to apply to gov jobs where I would take a 100k pay cut and no interview. The two interviews I did get were with reputable companies for very senior positions with high comp that I would say are high competition so maybe I’m doing something right but it feels hopeless. Anyone relate?

Some businesses plan weeks ahead while others plan months out. Curious what works best for different store sizes.

My team (retail snalytics) is analyzing genie for selfserve analytics, but we r regularly seeing high latency and timeout when it parses complex business logic or hits large Delta tables.
For prod: how are u keeping response times down ?

Hey everyone, I'm seeking some honest advice here. This is not an ad so i'm not dropping the name. Any help will be appreciated!!

I recently just finished building an analytical application. Think of it as an data analysis-focused desktop app with an audiance, between Excel and Power BI. Of course it has AI feature that can do analysis and generate a presentation with charts and insights based on the data. It's a desktop app because I know customers definitely want data-privacy.

I worked as a data analyst for 7 years and I poured everything I've learned into making this app great and actually digs out actionable insights. I might be in a tunnel-vision but I'd 100% use it to help me do better at my job.

My intuition is the target is small businesses and content creators who have some amount of data and have a need for data analysis, but at the same time they cannot afford to hire a professional analyst. However, I am desparate on what I should do to find the first 10 users.

If you have any advice please help, thank you all!

Revenue is easy enough to find, but things like true profitability or customer value can get complicated. What’s yours?

im applying for an in-house analytics role after four years at a services firm. My strongest project involved fixing a reporting pipeline for a large client, but I cannot name the client, exact revenue or their internal tools. I used multiple resume writers which suggested a polished impact bullet.

I stripped it back because it sounded more specific than i could defend. The version I have now says the industry, the scale in broad terms, the type of pipeline, and the reporting delay we reduced. It reads less impressive, but it is true and I can discuss the method

Hiring managers, is global retail client plus a bounded scale enough context? Or does anonymizzing everything makes a candidate's work feel generic no matter how real it was?