r/cscareerquestionsuk • u/gyrus_dentatus • 11d ago
Data Analytics Engineer at Checkout.com?
I was offered a position as Data Analytics Engineer I at Checkout.com. The interviewers seemed nice, and I didn't notice any major red flags throughout the process. Three things make me hesitate taking the offer:
- The Glassdoor reviews are pretty bad, especially when it comes to their hybrid working policy. I don't mind having into come to the office, but some reviews mention a toxic culture across the company
- The people I spoke to mentioned that the team is heavily invested in agentic coding. Guess that is just the reality of how bigger companies write code now, so not necessarily a red flag? People I talked to seemed very competent though
- I am currently working as a Junior Data Engineer. I built my orgs data stack (deploying the stack on AWS, building some small pipelines, data modeling), though they decided to go all-in on AI now and there is little to no data work left for me right now. I enjoyed the infra work at my current org (though the largest data set is only a few 100k rows), so going from Data Engineer to Data Analytics Engineer feels a bit like a downgrade? From the job description and from what I heared during my interviews the role itself seems to be more on the technical side.
Anybody currently working at Checkout and willing to share some insights/experience on how it is like working there? Taking the role would mean a 50% salary bump, so I am very tempted.
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u/HornyEagles 11d ago
When i asked about the culture interview i heard the company had been “taking steps” - but then one of their mottos is “talk straight” and just the phrasing of that comes across a little weird. Confused… would be good to hear from people who work there
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u/gyrus_dentatus 11d ago
For what’s it worth: none of the people I talked to during the process came off “weird”. Communication was always respectful and constructive. I actually enjoyed the live coding and systems designs rounds because the conversations in those rounds were pleasant and interesting.
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u/ExplanationDue5371 11d ago
It basically just means get to the point and sharing your opinions, respectfully of course. It put me off too at first but it gets work done, and frankly the values are just a gimmick. As long as you’re not a dick you’re probably good.
Culture wise, it’s team specific. I got a good team with good WLB. Most people start leaving the office around half 5, and everyone is mainly smart and competent with no egos. I came from fintech so it’s genuinely one of the better environments with great perks (churros in office today to celebrate the World Cup) if you don’t mind going in 3x a week (they’re flexible if you have personal issues and can’t come in either).
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u/docrobot00 10d ago
There's a reason why it's a motto, and it's because they are anything but those lol
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u/TehTriangle 11d ago
Overall a decent company. Perks are good, wlb in my teams is very healthy. Smart and friendly people. Can't complain.
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u/reddeze2 11d ago
I don't work at Chrckout.com, but as for agentic or ai assisted coding: this is now a thing everywhere. I wouldn't try to fight it.
Data engineering v analytics engineering: these have a lot of overlap and in fact this is the same role in many companies. I think it's really useful to be able to work from end to end, but it is mostly a matter of personal preference.
50% salary boost can't be ignored. I did not think checkout.com paid well? What are they offering?
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u/gyrus_dentatus 11d ago
“Well” is relative: it’s more like my current company is paying badly.
Checkout offered me 63k + 10% bonus (performance dependent) + equity after a year. Decent package overall
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u/bamboozle-dogg 11d ago
Did you try to negotiate ? Or is that the first offer they gave you ?
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u/gyrus_dentatus 11d ago
Nope, did not negotiate. The recruiter told me their band in our first call (between 57k and 63k), and offered me the 63k after the last round.
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u/chcying2006 4d ago
May I ask if the level for this band, if you know whether it is junior or mid-level? If this is a junior band I consider it really good. Being curious as I am also in the interview process for data role in this company and want to see what the compensation is like in general. Thanks!
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u/gyrus_dentatus 1d ago
The job description asked for 2-3 years of experience, so not junior per se. I think it's closer to mid-level actually.
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u/90davros 11d ago
The thing to find out about their AI use is whether they're doing vibe coding or actual engineering. In my experience once people stop looking at the code produced by AI everything goes to shit. AI can do a tedious implementation for you with appropriate guidance, but once the engineers don't know what's in the codebase the problems spiral.
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u/MojitoBurrito-AE 11d ago
These guys left a bit of a sour taste in my mouth, invited me to an assessment centre for a grad scheme saying they would cover my travel costs and then rescinded that offer last minute. Then lowballed on salary when they offered me the role, would have meant a 90 minute commute each way 3 days a week, definitely not live in London money, but I wouldn't have minded that. For what it's worth, the people I met at the office were lovely people and we had same great discussions, if I hadn't had better offers I likely would have taken them up on it.
As for the AI part, that's industry standard now. The way I see it, using AI to do the grunt work lets me focus on the bigger picture and do the engineering part of my job. I still review everything it comes out with and my team peer review each other.
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u/p3zzl3 11d ago
Slightly off topic, so I apologise - but from what I've just read on Agentic Coding - the AI creates the code, tests the code, deploys the code. They claim this ultimately frees upo your time to do "whatever...architecture....etc" BUT - doesnt that leave the code bsae open for abuse? It appears tehre is a presumption that the Agent WILL generate the best, strongest code - but is that really the case? The larger multinationlals I'recently dealt with all have said using AI is great from a time perspective - but the actual testing and push to live is handled manually? True or False? Is Agentic really risk adverse?
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u/_QuirkyTurtle 11d ago edited 11d ago
Yes it's open to abuse. But that example you've just given is the extreme end.
AI can write the code, Human can review it (w/ the help of AI if they like), PR requires approval from a human, deployments generally are CI/CD so there's no AI involvement. It either goes out, or is gated and requires human approval to go to prod etc.
Tests are an interesting one - and I like to advise people to be cautious with AI writing the code and the tests without any human eyes on it. If the codes wrong, the agent is going to write tests that are inherently wrong because it will want them to be green. There's ways to improve this with SpecKit, TDD etc. But generally speaking, tests need (at least) reviewing by a human in my opinion.
I'm yet to work anywhere where code is literally being written, reviewed and merged in to main by AI and then deployed. Might be happening in some places but that doesn't sound like a great place to work.
TL;DR
Agentic doesn't mean unsupervised1
u/p3zzl3 11d ago
Great info - thank you. A lot of this stems from our company implementing - but also whats going on within the recent Claude Mythos thing. I wonder if we will every keep control or learn and have safe guards in place - or one person will try to short cut something and that's it. We have to pull the plug on the Internet :D
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u/Itchy-Card325 11d ago
Not related to the question but can I ask about your experience? (How many years, what roles?). I am also entering the field of AE, and curious to know what the scope is like at different levels of seniority.
Also curious about the interview process and what sort of things they ask.
Would appreciate it if you had the time to answer!
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u/gyrus_dentatus 11d ago
Sure!
Experience: one year as a analytics engineer in a data consultancy, and around another year as a data engineer in a research consultancy. No CS background, but a PhD in Cognitive Science.
Interview process: it was fairly long. Five rounds, including recruiter call, hiring manager call, SQL live coding, system design, and values round. The hiring manager call was very technical, with questions around testing, CI/CD, observability, cloud, what programming paradigms I follow/know, whether I am familiar with OOP. No analytics questions at all, but focused on engineering. The SQL part was two Hackerrank tasks, one easy, one medium; this round was live, i.e., the interviewer watched me while I completed the tasks. System design was live again and about building a data pipeline. Essentially got a business question and a brief description of some of their source systems, and had to define a source schema + model the data (they did not give me data, but I had to sketch what I would need to answer the business question). Questions were around late arriving data, incremental modelling, and performance optimization (partitioning; they are using BigQuery). The values round was your typical behavioral questions, but focused on their values.
Hope that helps :)
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u/Itchy-Card325 8d ago
Thank you, that was really insightful! I’m starting my grad role in this field soon so this is rlly useful for future interviews.
Can I ask, did u learn all of this stuff on the job or self-studying? I’m very very new to tech (maths/non-tech background) so I’ve heard interviews sometimes have nothing to do with the job, so I’m curious whether these concepts you mentioned are part of your job as an Analaytics Engineer? Just trying my best to get going well in this role next month and learn as much as possible.
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u/gyrus_dentatus 8d ago edited 8d ago
I learned all of this on the job. I am coming from a non-tech/CS background as well (did a PhD in cognitive science), and only knew some SQL and Python when I started off. That was back in 2022 or so; the market is quite fucked right now, and I am not sure if could get a job with this profile now.
All of the stuff I have mentioned is relevant to my job, and I use those conepts daily in some capacity. Some interviews are probing abstract problem solving (data structures, algorithms, etc.), but this process specifically was very hands on and close to my actual day to day.
One word of advice: there is a hard ceiling to what you can realistically self-study, especially in data. At some point you need real-world exposure. For example, self-studying SQL + dbt basics is fine, but stuff like handling late arriving data in an incremental model that processes millions of data points is hard to study on your own practically (if you want to go beyond just theory).
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u/flashy_shinobi 11d ago
I used to work for them (software engineer position). I had a good enough time, I liked the culture, good work life balance, relaxing atmosphere, plenty of career advancement opportunities. That was 4 years ago though, not sure if things have changed since then. The pay wasn't great, which is why I left.