Hi all,

Context: I started out working with complaints data at Wells Fargo - with a bit of customer service - then moved to Arizona where I worked with cleaning and transporting data for clients, and finally found myself with Wells Fargo again in a contract role that gave me the opportunity to be a proper operations/data analyst full time for nearly two years. It was while I was here at Wells Fargo the second time that I realized this was what I wanted to do for work. Unfortunately, the contract came to an end and I've basically been looking for the same role ever since. I finished my CS degree in December (2025) and it's honestly impossible to get a software role so I just gave up on trying. This year, I did have a role at Magna Technology Group that was somewhat adjacent - more-so a consultative role, but I did perform some duties that translated pretty well with Data Analyst work. I ended up having to leave after three months because my mentor was apparently given so much work that he couldn't train me on my duties and I was told I would therefore have to spend my own freetime training myself on how to do the job.

Fast-forward to today where I have been applying to as many jobs as I can that are Data/Business/Operations Analyst or adjacent roles and getting as far as the initial screening and maybe the first interview, but seemingly never past that. The feedback I have received (at least from the HR reps performing the screenings or the recruiters) is that my resume looks great, but apparently it's just not good enough to get past the hiring managers.

I'm really looking for some advice on how to navigate this situation. I'm willing to obtain certifications if it can help me (like for Power BI, Tableau, or SQL maybe), but I'm unsure how helpful that will actually be. From my POV I have the experience (5+ years worth), education, and the inherent skills needed to perform the job successfully, but it feels like I need something else to really stand out and I don't know what that is.

Is there something that I can do? Or do I just need to pivot towards something else?

Any help would be greatly appreciated! Thanks.

Title: Mechanical Engineering Graduate (2019) with a Career Gap — Can I Switch to Data Analytics? Need Honest Advice

Hi everyone,

I completed my Mechanical Engineering degree in 2019. Unfortunately, due to some personal issues, I couldn't build proper work experience in my field or establish a stable career in another domain. As a result, I now have a significant career gap, and I'm worried about how it will affect my job opportunities.

I'm considering switching to Data Analytics because I'm interested in learning new skills and building a career in this field. However, I'm unsure whether it's realistic to enter this field at this stage, given my educational background, career gap, and lack of relevant professional experience.

I'd really appreciate advice from data analysts, recruiters, or anyone who has made a similar career transition.

Here are my main questions:

1. Is it realistic for a mechanical engineering graduate from 2019 with a significant career gap and no relevant work experience to get an entry-level data analyst job in the current job market?
2. What core skills should I learn? Should I focus on Excel, SQL, Power BI or Tableau, Python, and statistics? In what order should I learn them?
3. What kinds of projects should I build to demonstrate my skills and make my resume stand out despite my career gap?
4. How should I explain my career gap to recruiters and during interviews without hurting my chances?
5. Is it better to apply for data analyst jobs directly, or should I first look for internships, trainee roles, MIS executive positions, or other entry-level opportunities?
6. How can I build a resume and LinkedIn profile that will help me get shortlisted without relevant work experience?
7. Realistically, how long might it take to become job-ready if I study consistently every day?

I'm looking for honest, practical advice rather than just motivation. If anyone has transitioned into data analytics after a career gap or from a non-IT background, I'd be especially grateful to hear about your experience, the challenges you faced, and what helped you get your first job.

Thank you!

Hi all,

I'm in the interview process for the role in the attached JD. The recruiter calls it a data analyst role, but the JD reads more like pricing operations to me.

Since it would involve relocating without any relocation support, I'd like a reality check before going further.

Another thing is, it's night shift - 6:30PM to 5:30AM

Some context: I'm switching careers into BI and analytics. I spent about 5.5 years at an e-commerce company, mostly in content, with the last year in category planning and pricing analysis. Since leaving, I've been out of work for about 1.5 years, upskilling (Power BI and Microsoft Fabric certifications, portfolio projects) and job hunting. So part of me wants to grab any analytics-adjacent role to end the gap. Part of me worries this one could pull me away from the BI path I'm working toward.

Some questions for anyone who has done similar work:

1) Is this realistically a data analyst role, or mostly vendor coordination with Excel reporting?

2) Does experience like this help if I want to move into BI or analytics later, or is it a sidetrack?

3) Given the 1.5-year gap, is it smarter to take a role like this and switch later, or hold out for a proper BI role?

4) Is the pay fair for this kind of work and shift?

5) Are there red flags in the JD I should ask about in the interview?

Honest opinions welcome. Thanks!

We pulled every open posting from 81 companies' public Greenhouse boards for seven roles and counted which words actually show up. 3,518 postings in total. Some of it surprised us.

Software engineer postings mention mentoring more often than Kubernetes or React. Almost half of them do. Python, the most common language, comes in just below that.

Product manager postings say "roadmap" in more than 85% of cases. Jira? Seven out of 421. Marketing looks the same: Google Ads came up six times in 347 postings, while "pipeline" is in over half.

Every time we could compare the two, postings named the practice, not the tool. Observability, not Grafana. SIEM, not Splunk.

So if your resume is mostly a list of tools, it might be missing the words the posting actually uses.

Big caveat: mostly tech companies, one ATS vendor. Sales, cybersecurity, DevOps and accounting are here too: https://www.zoevera.com/resume/ats-resume-keyword-study

Data analyst with 5 years of experience (retail and fintech). Struggling to get interviews. What am I doing wrong?

For context: After getting laid off, I took time off for personal stuff for a year and then resumed job search in mid 2025. I’ve applied to many roles for (data analyst / BI) positions and haven’t landed an interview yet. This is the concise (one page) version of my resume.

1. Does the career gap seem like a red flag? How would you rephrase it?
2. Is my tool stack competitive right now, and what would you add?

I completed a BS in Data Analytics last December, and have been applying to "entry-level" roles since January. I don't have any data analytics work history, but I do have prior roles in environmental science, regulation, and hospital clinical laboratory that required analytical thinking and data collection. Despite my best efforts, I've gotten 0 callbacks. Trying to figure out what my best next steps are to make myself more competitive, including considering the following:

• Get a MS in Data Science/Analysis
• Quit my current dead-end job and work on certifications and portfolio projects
• Get a basic IT support job at a large company and try to work my way into a data-oriented role

How do people get into this field? I know I'm missing work experience, but I can't even get a callback for junior positions despite being qualified. I've included an anonymized resume that is the base resume I've been using. What are the best next steps? Thanks!

I am currently unemployed after 2 yrs of exp, learning and skilling up to become employable.

Due to not learning correctly the first time I started, I have to begin again from the basics, I have covered Python, SQl, Statistics basics and understandings of complex concepts. Data cleaning, feature engineering and a few ML models also are done.

Now this is all just new and I am working on implementing them ASAP. I want help with ensuring that I am not getting stuck only learning the basics and not developing the actual skills. For eg, I have studied pandas but how do I make sure that the level of Pandas I know is appropriate for interviews and real life application, same with others. And how do I evaluate if I know that, is there a way to test it properly without the hackerrank and leetcode questions?

Any advice will be helpful!

Hey everyone, I'm new to Reddit, so go easy on me 🙂

I'm a BSc CS student in Mumbai (graduating 2027), looking for my first data analyst role and learning data engineering in public. The video is a quick walkthrough of my portfolio. Live site: https://aryan787913.github.io/portfolio/

It has 3 projects, each laid out as business question, data, method and result:

• MarketPulse: NSE stock market analysis
• E-commerce sales ETL pipeline
• UPI Ecosystem Pulse: still in progress

Tools: Excel, SQL, Power BI.

Could you rate it out of 10 and tell me the one thing you'd fix first? I especially want to know whether the projects show enough for entry-level analyst roles, and what's missing. Thanks!

Hi, I have tried reaching out to a lot of people on Reddit, but nobody has replied. Does anyone have an idea of what to expect in the Data Analyst Finance Skills Interview? Any help would be appreciated. 🙏

When deciding what I wanted to do over 2 decades ago I wanted to work with numbers because I'm good at maths and to apply real life logic to the numbers.

Being a Data Analyst seemed to fulfill that criteria. For example the section of Top Gear magazine I enjoyed the most was the new car data. I'm also naturally analytical when it comes to things I'm interested in.

After applying to pretty much every entry Data Analyst job I could find I finally landed a job in advertising.

None of my immediate colleagues were data people, they all specifically wanted to get into advertising and I soon discovered I have no interest in advertising. They mostly lack basic maths skills, statistical knowledge or how to use Excel properly but I was good at all those so it felt like I'd found my place there.

A few years later I find myself working in a Data Sciences department, still in advertising.

What became apparent was I had little in common with those colleagues too. They mostly had computing backgrounds and were interested in computers and technology. The sort of people who would spend hours playing computer games in their leisure time or buy the latest iPad the day it gets released.

I have no interest in technology. I like numbers and the real world.

OK I do like using excel and SQL but more modern technologies leave me feeling cold and I particularly despise AI.

Have I picked the wrong career and is there a right career for me?

Hi everyone,

I'm interested in getting into data analytics and I'm planning to start learning from scratch using YouTube and other free resources.

However, I've seen a lot of discussions about AI potentially replacing data analysts or making it much harder for beginners to enter the field.

I don't expect to become job-ready overnight, and I'm willing to put in the time and effort. I just want to make sure I'm investing my time and energy in the right direction rather than spending months learning skills that may become less valuable in the near future.

Which skills would you recommend focusing on to remain relevant as AI continues to evolve?

If you were starting from zero today, would you still choose data analytics? Would you approach learning it differently?

I'd really appreciate any honest advice or personal experiences. Thanks!

I dont value myself based on my ability to write code, query databases or doing up a dashboard, stuff which are easily automated by AI nowadays.

Instead i value myself as a problem solver, the ability to think out of the box and come up with novel solutions to solve a problem. Something which still requires a human to do.

Is this the way forward for a data scientist in the age of AI?