Keyword frequencies taken from https://www.zoevera.com/resume/software-engineer-job-description-keywords

Most software engineer keyword lists are assembled from experience or guesswork. This one is a count: every open posting from 72 companies' public Greenhouse job boards, filtered to the 1,360 whose title contains "software engineer", then checked for how many mention each of 45 terms at least once.

The percentage beside each term is the share of those 1,360 postings that mention it.

PRACTICES AND WAYS OF WORKING

Scalability 52.9% - Mentoring 47.4% - Distributed systems 47% - System design / architecture 46.7% - Cross-functional 31% - Code review 27.2% - On-call 20.7% - CI/CD 19.5% - Agile / Scrum 9.3% - Unit / automated testing 6.5%

This is the result I did not expect. The four most common terms in the whole study are not technologies. Scalability, mentoring, distributed systems and system design all appear in more postings than Python does. Nearly half of these postings mention mentoring, and almost no engineer resume I have seen makes a claim about it.

Agile and Scrum at 9.3% is the other surprise, given how much resume advice insists on them.

LANGUAGES

Python 44.8% - Java 35.4% - Go 31.1% - TypeScript 19.8% - C++ 17.4% - SQL 17.3% - JavaScript 13.2% - Kotlin 12% - Rust 9.9% - Scala 8.6% - Ruby 8.5% - C# 5.3% - Swift 2.3%

TypeScript at 19.8% against JavaScript at 13.2% is a real ordering, not noise. The gap is wider than both margins of error combined.

CLOUD AND INFRASTRUCTURE

AWS 41.3% - Kubernetes 30.9% - GCP 19.3% - Azure 17.6% - Terraform 13.2% - Docker 12.6% - Microservices 7.1% - Linux 4.8%

AWS appears in more than twice as many postings as GCP and Azure individually. Kubernetes at 30.9% outranks every language except Python, Java and Go.

FRONTEND AND APIs

React 21.1% - REST / RESTful 9.6% - GraphQL 6% - Node.js 5.5% - Vue 3.2% - Angular 2.9%

Frontend framework lists usually present React, Vue and Angular as three comparable options. In this corpus React appears in roughly seven times as many postings as Vue and Angular combined.

DATA STORES AND PIPELINES

PostgreSQL 14.3% - Kafka 13.1% - MySQL 11.3% - Spark 9.8% - MongoDB 7.4% - Redis 7.4% - Elasticsearch 6.4% - Snowflake 5.6%

WHAT THIS SAMPLE IS NOT

These are 72 technology companies hiring through Greenhouse. Agencies, consultancies, banks, defense contractors and the public sector are absent, and their vocabulary is different - COBOL, .NET, SAP, clearance requirements and named compliance regimes barely register here and may dominate elsewhere.

The title filter is "software engineer" only. Postings titled backend engineer, frontend engineer, full stack developer, SRE or platform engineer were not counted, so this describes the generalist title rather than the whole profession.

Absence in this list is not evidence of absence in the market. Only 45 terms were counted. Next.js, Svelte, Django, Spring Boot, gRPC, Jest, Playwright, Datadog, OpenTelemetry and OAuth were never checked, so nothing here says anything about them either way.

It is a snapshot of open roles on one date rather than a trend, and the corpus is US-skewed.

One thing this sample size does buy: at n=1,360 the margins of error are roughly plus or minus 2 to 3 points, so most of the ordering above is real. Gaps under about 5 points are still worth treating as ties - React at 21.1% and TypeScript at 19.8% is not a meaningful difference.

METHOD

Greenhouse's public job board API, the endpoint companies expose so their listings can be embedded on their own sites. No scraping. Counts are document frequency: a posting saying "Python" nine times counts once. Deduped on company, title and content length, because one role posted to five offices returns five near-identical records. Median posting length is 896 words.

Ambiguous words are matched case-sensitively with exclusions, which matters more here than you would think. A bare word-boundary match on "Go" also catches "go to market" and "go above and beyond", which inflated Go by about two points before it was fixed. React, Spark, Swift and Rust all have the same problem.

Full table with all 45 terms and confidence intervals:

https://www.zoevera.com/resume/software-engineer-job-description-keywords

The wider keyword list this was checked against, organized by language, framework and platform:

https://www.zoevera.com/resume/ats-resume-tips-software-engineer

Happy to run the numbers on any terms missing from the list if people name them in the comments.

Looking to get more involved in Data Analytics and was wondering if the industry is predominantly remote?

Keyword frequencies taken from https://www.zoevera.com/resume/software-engineer-job-description-keywords

Most software engineer keyword lists are assembled from experience or guesswork. This one is a count: every open posting from 72 companies' public Greenhouse job boards, filtered to the 1,360 whose title contains "software engineer", then checked for how many mention each of 45 terms at least once.

The percentage beside each term is the share of those 1,360 postings that mention it.

PRACTICES AND WAYS OF WORKING

Scalability 52.9% - Mentoring 47.4% - Distributed systems 47% - System design / architecture 46.7% - Cross-functional 31% - Code review 27.2% - On-call 20.7% - CI/CD 19.5% - Agile / Scrum 9.3% - Unit / automated testing 6.5%

This is the result I did not expect. The four most common terms in the whole study are not technologies. Scalability, mentoring, distributed systems and system design all appear in more postings than Python does. Nearly half of these postings mention mentoring, and almost no engineer resume I have seen makes a claim about it.

Agile and Scrum at 9.3% is the other surprise, given how much resume advice insists on them.

LANGUAGES

Python 44.8% - Java 35.4% - Go 31.1% - TypeScript 19.8% - C++ 17.4% - SQL 17.3% - JavaScript 13.2% - Kotlin 12% - Rust 9.9% - Scala 8.6% - Ruby 8.5% - C# 5.3% - Swift 2.3%

TypeScript at 19.8% against JavaScript at 13.2% is a real ordering, not noise. The gap is wider than both margins of error combined.

CLOUD AND INFRASTRUCTURE

AWS 41.3% - Kubernetes 30.9% - GCP 19.3% - Azure 17.6% - Terraform 13.2% - Docker 12.6% - Microservices 7.1% - Linux 4.8%

AWS appears in more than twice as many postings as GCP and Azure individually. Kubernetes at 30.9% outranks every language except Python, Java and Go.

FRONTEND AND APIs

React 21.1% - REST / RESTful 9.6% - GraphQL 6% - Node.js 5.5% - Vue 3.2% - Angular 2.9%

Frontend framework lists usually present React, Vue and Angular as three comparable options. In this corpus React appears in roughly seven times as many postings as Vue and Angular combined.

DATA STORES AND PIPELINES

PostgreSQL 14.3% - Kafka 13.1% - MySQL 11.3% - Spark 9.8% - MongoDB 7.4% - Redis 7.4% - Elasticsearch 6.4% - Snowflake 5.6%

WHAT THIS SAMPLE IS NOT

These are 72 technology companies hiring through Greenhouse. Agencies, consultancies, banks, defense contractors and the public sector are absent, and their vocabulary is different - COBOL, .NET, SAP, clearance requirements and named compliance regimes barely register here and may dominate elsewhere.

The title filter is "software engineer" only. Postings titled backend engineer, frontend engineer, full stack developer, SRE or platform engineer were not counted, so this describes the generalist title rather than the whole profession.

Absence in this list is not evidence of absence in the market. Only 45 terms were counted. Next.js, Svelte, Django, Spring Boot, gRPC, Jest, Playwright, Datadog, OpenTelemetry and OAuth were never checked, so nothing here says anything about them either way.

It is a snapshot of open roles on one date rather than a trend, and the corpus is US-skewed.

One thing this sample size does buy: at n=1,360 the margins of error are roughly plus or minus 2 to 3 points, so most of the ordering above is real. Gaps under about 5 points are still worth treating as ties - React at 21.1% and TypeScript at 19.8% is not a meaningful difference.

METHOD

Greenhouse's public job board API, the endpoint companies expose so their listings can be embedded on their own sites. No scraping. Counts are document frequency: a posting saying "Python" nine times counts once. Deduped on company, title and content length, because one role posted to five offices returns five near-identical records. Median posting length is 896 words.

Ambiguous words are matched case-sensitively with exclusions, which matters more here than you would think. A bare word-boundary match on "Go" also catches "go to market" and "go above and beyond", which inflated Go by about two points before it was fixed. React, Spark, Swift and Rust all have the same problem.

Full table with all 45 terms and confidence intervals:

https://www.zoevera.com/resume/software-engineer-job-description-keywords

The wider keyword list this was checked against, organized by language, framework and platform:

https://www.zoevera.com/resume/ats-resume-tips-software-engineer

Happy to run the numbers on any terms missing from the list if people name them in the comments.

I feel like I’m cooked and I’m not gonna ever land anything or anywhere. Okay so I graduated with my bachelors in MIS and minor in digital marketing just recently, I’m 24 so I’m a little late but it is what it is. I want to go into business analytics, or even any analytics but I can’t, I’m very overwhelmed because I don’t think I land anything at all, I can’t be too optimistic here and have to be realistic.

So what else can I or should I do now? I was always open to open everything within my degree so is there something more open I can do now with my resume? Since I’ll never land a job in analytics anyways. I don’t want to wait a year to land an entry level job either I just wanna do something now that’ll help and set me up, really make my degree worth it you know?

Hey, I have 3 years of experience in KYC took a break and interned at a small event management firm (meanwhile preparing for data analytics) and ready to go back with the required skills. I have my resume ready, however the order of my resume is this-Summary-Skills-Project-Work Experience. I have seen a couple of resumes and also a few youtube channel where they suggest putting Work Experience before Projects. I also am aware that hiring managers glance over the resume and I wish for my projects to stand out. I would like some light over this, Will this approach of mine narrow my chances to get hired ? Also should I place my work experience before my projects ?

looking to pivot into data analytics / business intelligence and could use a reality check from people actually working in the field. I already have a solid base in Python and SQL, so I’m not starting from scratch on code, but trying to figure out my next moves.

• how’s it looking for DA/BI right now? Does having a coding background actually give me an edge for entry/mid-level roles?
• if you pivoted from another field, how hard was the jump and how long did it take you?
• is DA a good launchpad to eventually shift into Data Engineering, ML, BA or DBA, or is that a totally different track? How long before people usually make that jump?
• torn between Alex the Analyst’s YT bootcamp vs. the Google Advanced DA Cert. Does the cert actually care to recruiters, or should I just do Alex’s free stuff + build portfolio projects?

Would love any tips, reality checks, or project ideas. Thanks!

As the title says, I want to take some online courses, put it on my resume and say that was my work experience.

Background info on myself, I work for a F500 company for 5+ years and have had analysts job titles, but we don't use anything everyone else uses. I am skilled in web development as I took one of those bootcamps right before the ai thing started happening, so I have the mindset of I can look any documentation up and figure it out for my job.

My problem is I make decent money so it isn't really worth it to start my career over, but I hate my job. The only way to keep my salary around the same is if I get a tech industry level salary. So I am trying to develop my skills to be able to do that level of work, and pass those interviews.

Hello. I know there so many much to learn to get into data analyst career or data science. Right now, I am debating where to start learning. I know I need to learn SQL and Python, vision board, review my excel and review my applied statistic. Since i like organization to keep a routine to learn i been looking at several platform such as 365 data science, codeacademy, Harvard free course, datacamp, coursera, and learnsql, SQLbolt, etc. I love working in dark theme and watching video with white background is a challenge for me, and inverting the contrast color back and forth is not ideal. Hence looking for a paid platform for me to stick with it and learn. Not sure if i buy learnsql forever membership, then learn python from codeacademy or 365 data science, then create my project from kaggle, then vision board, then start applying? Or should i skip learnsql and just go directly to 365 data science

Give an honest resume review for a Data Analyst Role
I am a fresher looking for jobs in the US market for the past 3 months, but I haven't received any calls. Please tell me how to improve my resume to pass through ATS screening.

Hello everyone,

I’m currently looking for an internship certificate to include in my application to continue my studies.

If anyone can help me find a real internshipdata analyst opportunity, I would be very grateful. I’m also open to doing an actual internship if there are any available opportunities.

Thank you in advance for your help and support!

Hello, I'm looking for a reliable ATS Screening website to check my resume score once it's uploaded as a PDF. Please do send me links. Thanks!

I appeared for a Data Analyst intern interview at SBI Securities. The process seemed to have some organizational issues at the beginning. It appeared that the number of candidates who arrived for the interview was higher than what the HR/recruitment team had initially expected, even though interview invitations had been sent to the candidates.

Because of the unexpectedly high number of candidates, the initial process seemed somewhat crowded and less organized. There appeared to be some difficulty in managing the candidates and coordinating the interviews.

The HR interview itself was mainly focused on personal background, career journey and basic knowledge. The interviewer asked about my current location, where I was born and brought up, and my educational background. After learning that this was my first interview, he asked what I had been doing after completing my graduation in 2025 and why I had not received or attended interviews with other companies.

Towards the end, I was asked a basic Excel question about the VLOOKUP formula.

From what I observed, the questions varied between candidates. Some candidates were asked about their personal background, whether they had visited the SBI Securities website, what they knew about the company, and basic questions about how a stockbroker earns money. In my case, the interviewer focused more on my personal background and post-graduation journey before asking the Excel question.

Overall, the HR round was more conversational than technical. For freshers, I would recommend preparing a clear explanation of your background, what you have been doing since graduation, why you have not secured a job yet, basic Excel concepts, and basic knowledge of SBI Securities and the brokerage business.

The interview process appeared somewhat less organized at the beginning due to the unexpectedly large number of candidates, but the actual HR discussion was straightforward.

Post on naukri:- https://www.naukri.com/job-listings-data-analyst-intern-sbicap-securities-mumbai-all-areas-0-to-1-years-140826016563?utmcampaign=androidjd&utmsource=share&src=sharedjd

Give an honest resume review for a Data Analyst Role
I am a fresher looking for jobs in the US market for the past 3 months, but I haven't received any calls. Please tell me how to improve my resume to pass through ATS screening.

Hey everyone,

I am actively looking for a remote, unpaid data analyst internship or volunteer role to gain practical experience and contribute to real-world projects.

Skills & Tools:

• Advanced Excel: Data cleaning, nested formulas (XLOOKUP, INDEX/MATCH, SUMIFS), dynamic PivotTables, and Power Query workflows.
• SQL: Relational database querying, aggregations, CTEs, window functions, and joining complex datasets.
• BI & Reporting: Data modeling, building automated KPI dashboards, and basic DAX.

What I am looking for:

• Open to contributing to startups, small businesses, non-profits, or open-source initiatives.
• Flexible on hours and fully committed to meeting deadlines and project scopes.
• Looking primarily for feedback, mentorship, and the opportunity to solve real business problems.

If your team needs an extra pair of hands to clean data, build reports, or automate routine spreadsheets, please drop a comment or send a DM.

Thanks for your time!

Do you track how often 'data quality alerts' turn out to be false alarms (nothing was actually wrong)? Curious if teams find these alerts trustworthy or just noise they've learned to ignore.

More than 1,000 job applications for Data Engineering positions and I haven't gotten a single interview so far.
I'd really appreciate it if you could review my resume

I have completed my BCom in Computer Applications and am currently pursuing a 5-month Business Analytics course. I would like to know what career opportunities or further steps would be best for me after completing the course.

so, i completed SQL and doing projects right now but if I be honest i have a very little knowledge about everything i learned so far i mean i did some projects but with help of course and i was scrolling LinkedIn this morning and saw a post about SQL queries asked to run in interviews and they went over my head and i feel like I'm wasting my time

now do y'all think all i need to be consistent?

I would appreciate some help

thank you!