perfume and cologne
#n
night bramble
ur resume looks good :)
do u do good in OAs?
if ur not getting 100/100 thats the problem.
with the amt of competition... even that wont guarantee a phone
vestal yew
In your two latest jobs you're not mentioning what languages or frameworks you're using in your bullet points. You name drop cloud services but not what you used to code things up?
Also remove AMA section, I used to have a "hobbies" section but someone told me it introduced bias and it's not worth the risk. Up to you really
And rename TECHNOLOGIES to SKILLS
night bramble
It might just be more ops related. Not all SWE is coding, tho it should be to a degree.
I do agree tho, if a language was used, even if it was minimal, include it.
AMA does need to go tho
vestal yew
Oh and make sure your work experience has locations too
merry ocean
I would put techs before experience, maybe even before education 🙃
I agree with others: for your experience section, add some langs in parens like you did with projects
Big Bank -> last bullet Instead of 'implemented', did you maybe 'lead an initiative to adopt docker and kubernetes for <the problem it solved>" because if you can say that you get big fat plus points. If you can't say that, then at least I'd like to know what context you're using docker and kubernetes (for microservices? scaling? deployment?) and why both? anyways yeah that bullet just seems vague
Big Tech not FAANG
-> python error coverage (?) data engine (?) I want more specifics. do you mean you added exception handling? more unit tests?
second bullet -> very strong, consider leading with it
Big Bank
first bullet -> internal facing tool or customer facing tool?
Projects
Dates? need context for if they were school projects, side projects, or if they were projects at some employer, consider adding them as project headings under the employer
If they're all school projects you can just head it Projects - Boston University of Science and Knowledge or whatever
And generally (nitpik) I don't love the "programmed" "coded" verbs, but I get it you're running the risk of saying 'developed' or 'engineered' n+1 times.
Still - Programmed agglomerative clustering -> Leveraged scikit-learn's AgglomerativeClustering to develop [system] (unless you really built it from the ground up, then what scikit learn api did you inherit your class from, Estimator probably?]
But overall you have some very very strong points:
- improving development speed by x%
- saving $k / mo with optimized query use
- the mythical error-free ci/cd pipeline