What I Learned from 11 Python/Web Interviews: Tips, Mistakes, and Must‑Know Questions

Recently I quit my job in Shanghai and embarked on an intensive interview hunt, documenting my experiences and lessons learned.

Resume preparation : I created two versions of my resume—one for data analysis and one for web full‑stack development—highlighting skills such as frontend, Django, web crawling, data analysis, machine learning, NLP, and Linux.

Interview scheduling : By submitting applications from two accounts across multiple platforms, I received 2‑4 interview invitations daily and arranged 2‑3 interviews each day, often scheduling them close together to save time.

Company interviews and outcomes :

Data Analysis Algorithm Engineer – required Hive knowledge I lacked; not passed.

Algorithm Engineer – interview with general manager; I explained my web, crawling, and ML background but the role was not a fit.

Bio‑system Development Engineer – offered; used Django and Docker, with data‑analysis tasks.

Python Full‑Stack Developer – small company, required both front‑ and back‑end work; interviewers discouraged me from joining.

Python Developer – multiple products, three‑round interview (technical, HR, CTO); awaiting result.

Crawler + Data Analysis – interview focused on data collection; no technical questions, likely rejected.

Web Full‑Stack Developer – offered; used Django, Flask, Bootstrap, and ECharts for a monitoring site.

Test/Automation Engineer – Python scripting for testing hundreds of phones; role leaned toward testing.

Python Development Engineer – multiple interview rounds with technical and HR directors; final round pending.

Web + Data Analysis (Vue.js + Tornado) – detailed technical first round; preparing a PPT for the second round.

Web + Data Analysis (Vue.js + Flask) – salary expectations were too high; interview ended after discussion.

Interview takeaways :

Familiarize yourself with popular technologies such as Vue.js, RESTful APIs, Node.js, Docker, and front‑back separation.

Web full‑stack development (Django/Flask/Tornado + Vue/Node) combined with data analysis is highly marketable.

Combining web development with data‑science skills (including crawling) boosts employability.

Test automation roles value candidates who can code scripts and understand testing tools.

Pure crawling or pure data‑analysis positions demand strong project experience and solid algorithm fundamentals.

Record every interview question you encounter; reviewing unanswered questions later reveals common patterns and helps you prepare more effectively.

Recent interview questions (selected) :

Differences between TCP, UDP, and HTTP.

Deep copy vs. shallow copy.

Front‑end request handling flow among uWSGI, Nginx, and Django.

Redis data structures you have used and their persistence.

Celery task queue basics.

Difference between ModelFirst and DBFirst.

Thread vs. process vs. coroutine.

Tornado framework overview.

One‑hot encoding and data binning.

Stack vs. heap memory.

Common sorting algorithms.

MySQL optimization and multi‑table queries.

Finding files in Linux.

Closures in programming.

Django model inheritance.

Updating timestamps in models.

Settings configuration in Django.

CSRF protection for AJAX requests.

Insights on machine data analysis and modeling.

Crawler principles.

Why Redis is fast beyond being in‑memory.

Differences between Python 2 and Python 3.

Challenges when migrating a Python 2 project to Python 3.