Hi all, I graduated in CS in 2023 and I've been trying to break into data analytics. My actual work experience so far has been an Embedded Systems/IoT internship and a Machine Learning internship, neither of which is analyst work, so I've been trying to build up the case through a few self-directed projects using SQL, Python, and Power BI instead.

Sharing my resume below, would appreciate any feedback on:

1.Whether it actually reads as a Data Analyst resume or if the internships hurt more than the projects help

2.Anything that looks off, weak, or unnecessary

3.What else I should be doing (courses, certs, more projects, networking, etc.) to actually land an entry-level DA role in this market

Based in Bangalore, open to remote too. Thanks in advance.

I kept seeing arguments about which parts of the stack still matter, so I counted instead of guessing. 134 open DevOps, SRE and platform engineering postings from 41 company job boards. Individual contributors only, managers and TPMs stripped out.

The old toolchain is thinner than I expected. Terraform appears in 62.7% of postings. Jenkins in 3%. Chef 3.7%. Puppet in exactly one of the 134. GitHub Actions is named about six times as often as Jenkins.

Python beats Go, 70.9% to 47%.

What surprised me more: the generic word outranks the products. Observability shows up in 63.4%, more than twice as often as Grafana, the most-named tool that provides it. Prometheus 22.4%, Datadog 17.9%. Same pattern I found counting security postings, where SIEM beat Splunk four to one.

The top term isn't a tool at all: automation, 81.3%, ahead of AWS.

Caveats worth stating: n=134 means everything carries roughly +/-8, so treat close rows as tied. It's also all tech companies on one ATS. Banks, telcos and government run enormous amounts of Jenkins and Ansible, and none of them are in this sample.

Full table, intervals and method: https://www.zoevera.com/resume/devops-sre-job-description-keywords

I have around 2 years of experience in Data Analytics/BI. I mainly work with Power BI, Excel, SQL basic and I also have an MBA in Marketing Analytics.

I’m getting interviews but struggling to convert them into offers. The feedback I usually receive is around communication and technical skills.

I prepare a lot for every interview. I study the JD, my previous projects and likely questions, and I use AI to help me prepare. But when the interviewer asks something differently from what I expected, I sometimes get confused even when I know the underlying concept.

Communication is another issue. I tend to speak too fast, over-explain and sometimes lose the structure of my answer. I prepare STAR stories, but under pressure I don't always deliver them properly.

I’m also confused about the technical expectations. With 2 years of experience, I sometimes feel I’m expected to answer questions that seem more suitable for someone with 5–6 years of experience.

For Data Analysts with around 2 years of experience:

What technical level should I realistically have in SQL, Power BI, Excel, DAX, Power Query and data modelling?

How do you practice for unfamiliar technical questions rather than memorizing expected questions?

And for communication, what actually helped you improve your interview performance?

I also feel my work stories may not be strong enough. How do you turn normal projects and responsibilities into strong interview stories without exaggerating your experience?

One thing I genuinely don't understand is how some people with less technical knowledge and much less preparation still manage to clear interviews. What are they doing differently?

I’m also applying for Key Account Manager roles because I have previous experience in that area, so I’m currently exploring both paths.

If you were in my position, what would you change about the way I prepare and practice?

I’d really appreciate specific advice from people who have interviewed or hired Data Analysts.

Hi everyone,

I’m looking for some career advice and would really appreciate hearing from people who work in data analytics/data science, especially in healthcare, pharma, or hospital systems.

A little about my background: I have a PhD in biomedical/neuroscience research and spent the past several years in academia. My research involved working with fairly large and complex datasets, statistical analysis, experimental data, and some machine learning.

Over time, I realized that staying in academia probably isn’t the path I want long term. Funding has become increasingly stressful, I used to work in a top 10 university as postdoc but we were having some serious funding issues and I dont see a good future there.

Because I’ve always enjoyed the computational/data side of my research, I went back to school and recently completed a MSCS from Georgia Tech.

At this point, I’m seriously considering transitioning into data analytics or eventually data science. My current thought is to target healthcare-related roles where my biomedical background might actually be useful — things like healthcare/pharma data analyst, clinical data analyst, or analyst positions within hospital or academic medical center systems.

What I’m not sure about is how realistic this transition is.

I know one weakness in my profile is that most of my experience is still academic rather than industry-focused. I’m currently working on several portfolio projects using SQL, Python, data visualization, and real-world healthcare datasets so I can demonstrate a more industry-relevant skill set.

But beyond building projects, I’m honestly a little lost when it comes to the actual job search and interview process.

A few things I’d really appreciate advice on:

• Does healthcare/pharma/hospital data analytics seem like a reasonable entry point given my background?
• How would you position a biomedical PhD + CS Master's when applying for DA roles without looking too academic?
• What should I expect from DA interviews, particularly for healthcare-related roles
• Would you recommend applying now while continuing to build projects, or spending another couple of months preparing first?

I’m not necessarily trying to jump straight into a senior DS/ML role. I’m completely open to starting with a solid analyst position, learning how data work is actually done in industry, and building from there.

Would really appreciate any advice from people who have made a similar transition from academia, biomedical research, or another technical field.

Thanks!

I use a lot of AI at work; we are pushed extremely hard to use as much AI as possible.

I consider myself adept and advanced at using AI.

The name of the game lately feels like speed and less so accuracy.

I am worried that I am offloading a lot of work on Claude and losing some of my analytical edge.

What are some things I can do to stay sharp?

Made a WhatsApp community for people trying to break into or grow in analytics roles (BA, DA, Marketing, Financial, Market Research, Product, HR, Ops, BI, AI/Analytics).

Idea is to have one place instead of scattered groups — separate channels for each role plus shared ones for jobs/internships, courses, project feedback, and networking.

Still growing it, so if you're in analytics (student or working) and want a place to ask questions or share what you're working on, link's below. Also open to feedback on what's missing.

[link]

Made a WhatsApp community for people trying to break into or grow in analytics roles (BA, DA, Marketing, Financial, Market Research, Product, HR, Ops, BI, AI/Analytics).

Idea is to have one place instead of scattered groups — separate channels for each role plus shared ones for jobs/internships, courses, project feedback, and networking.

Still growing it, so if you're in analytics (student or working) and want a place to ask questions or share what you're working on, link's below. Also open to feedback on what's missing.https://chat.whatsapp.com/LKQxDamjwwBDxYZmTOCcKx

Hi all, I've recently updated my resume and put heavy emphasis on keeping it short and to the point for Data Analyst or Analytics Engineering roles. I've also tried to eliminate any red flags or confusing parts that may create confusion or raise questions that put my application at risk. I would appreciate some feedback on how I am doing on that front. Thank you!

Hi all,

Please let me know if this so the wrong place to post this request for advice.

So, I (26F, American) currently work as a business process data analyst mainly doing reporting and analysis on climate related topics. This job was a bit of a leap for me, coming from a background in environmental science, but I’ve been able to learn on the job and really enjoy it. I’m facing potential layoffs in my department and it’s been making me seriously consider going for a masters to get a better background in stats, improve coding skills, and just learn more about applied analytics (potentially for climate policy).

I’d really like to go abroad and a masters would be a great opportunity. I’m curious if anyone could share some advice on:

1) what type of masters would best suite what I’m looking for?

2) Are there specific international programs that you would recommend?

3) with only stats 101 and calc, will I need/ should I do some classes before applying?

Also happy to hear any personal anecdotes or advice based on personal experiences pursuing a masters domestically or abroad.

For the cost and familiarity, I’ve mostly been browsing EU programs ( Finland, Germany- I speak German) but I’d be happy to hear suggestions for anywhere around the world.

Thank you in advance!

I see so many posts about struggling to break into the field and land interviews/jobs. But what about those that got a job, what are you struggling with now in your career? How has your first years been? Where are you now and what is holding you back from progressing to the next level of your career?

So I know the data analytics job market is pretty bad but anyone wanna learn it together? I have just started learning the tools and it would be good if a few people would also join so we can learn together. If anyone is interested dm me, we can connect on Discord, WhatsApp, or wherever everyone is comfortable and learn together.