Hardik Linzara
Analyst
C++
Python
SQL
C
JavaScript
Active 10 hours ago
+91 (9996) 474349
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Overview
Technical skills
Timeline
Roles
Overview
Backend developer (Middle) focused on building an end-to-end Streamlit-backed personalised e-commerce experience with a clear focus on UX and product flows. The strongest proven skill is implementing application-level business logic and DB helpers that power shop, product and cart workflows, demonstrated by the get_db, upsert_customer, log_purchase and discount functions in the application code. Not evidenced are production-grade concerns such as automated testing, schema migrations, robust error handling, secure credential handling, or multi-service architecture.
Technical skills
C++
Python
SQL
C
JavaScript• Junior
Databases
MySQL
Frontend
Frontend
UI
DevOps
Git
GitHub
Azure
AI/ML
Computer Vision
NumPy
Pandas
Scikit-learn
Streamlit
Analytics
Matplotlib
Power BI
Seaborn
Tableau
Timeline
Web Development Intern
•
Junior
Prodigy Infotech
•
Internship
Built and shipped responsive front-end UI components for client-facing projects using HTML, CSS, and JavaScript. Worked in a team environment with tight deadlines, translating provided specs into functional interface features. Refactored front-end styles to improve page load performance across multiple project pages.
JavaScript
Middle Backend Developer
Confidence: High API Engineer
Backend developer (Middle) focused on building an end-to-end Streamlit-backed personalised e-commerce experience with a clear focus on UX and product flows. The strongest proven skill is implementing application-level business logic and DB helpers that power shop, product and cart workflows, demonstrated by the get_db, upsert_customer, log_purchase and discount functions in the application code. Not evidenced are production-grade concerns such as automated testing, schema migrations, robust error handling, secure credential handling, or multi-service architecture.
API Design
2/10
How well APIs are designed
Minimal API design considerations; UI-driven handlers call DB helpers directly with no versioning, formal error contract, or idempotency beyond upsert usage.
Evidence
smart-e-commerce-personalized-offer-system/app.py: _customer_login and _owner_login drive flows directly without an API layer
smart-e-commerce-personalized-offer-system/app.py: upsert_customer used as an idempotent upsert but no explicit idempotency key or documented error contract
Data Layer & Database
3/10
Working with databases
Basic data-layer usage of MongoDB with sensible helpers and upserts but no migration history, explicit transaction boundaries, or tuned queries/indexes.
Evidence
smart-e-commerce-personalized-offer-system/app.py: get_db uses MongoClient with serverSelectionTimeoutMS and returns a database handle
smart-e-commerce-personalized-offer-system/app.py: upsert_customer and save_owner use update_one with upsert=True; log_purchase performs insert_one
Scalability & Performance
2/10
Handling load and speed
Some performance awareness through streamlit caching for DB/model but no broader scalability patterns like caching invalidation, queueing, or connection pooling strategies for high load.
Evidence
smart-e-commerce-personalized-offer-system/app.py: @st.cache_resource applied to get_db and load_model
smart-e-commerce-personalized-offer-system/requirements.txt: includes scikit-learn and joblib indicating local model inference via load_model
System Architecture
2/10
Overall system structure
Single-file Streamlit application with separated helper functions but no deliberate service decomposition, configuration management, or multi-service contracts.
Evidence
smart-e-commerce-personalized-offer-system/app.py: file organizes DB helpers, discount engine, session state and UI flows in distinct functions within one module
smart-e-commerce-personalized-offer-system/app.py: get_db reads secrets from streamlit or environment but there is no external config management
Security & Auth
2/10
Protecting data and access
Basic secrets usage for DB URI shows some awareness but critical gaps exist such as plaintext password storage, minimal input validation and lack of authentication hardening.
Evidence
smart-e-commerce-personalized-offer-system/app.py: get_db checks st.secrets and os.environ for MONGO_URI
smart-e-commerce-personalized-offer-system/app.py: owner password comparison uses plaintext (owner.get('password') == opwd) in _owner_login
Reliability & Observability
2/10
Stability and monitoring
Limited reliability patterns; a DB client timeout is set but DB operations lack try/except, there are no retries/backoff, circuit breakers, structured logging, or graceful shutdown handling.
Evidence
smart-e-commerce-personalized-offer-system/app.py: MongoClient created with serverSelectionTimeoutMS=5000 in get_db
smart-e-commerce-personalized-offer-system/app.py: most DB helpers (add_product, delete_product, update_product, log_purchase) call PyMongo methods without error handling
Expertise
Backend AI & LLM• Middle
Databases & Vector Storage• Middle
Industries
Commerce• Middle
Recommendations
- Harden authentication and secrets - avoid plaintext passwords, add password hashing and a proper credential lifecycle with token management or delegated auth.
- Add robust error handling and retries around DB operations, and introduce logging and basic observability (request/operation correlation ids and metrics).
- Split responsibilities into a small service/api layer for business logic and a separate UI layer, and introduce DB migrations / schema evolution tooling and tests.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Junior Frontend Developer
Confidence: Medium Generalist
A junior web developer focused on building static client-side interfaces using HTML, CSS, and vanilla JavaScript. The strongest proven skill is basic client-side interactivity demonstrated by the showWeather function and weatherData object in Hardiklinzara/prodigy_wd-04/weather_data_API.html. There is little evidence of modular architecture, modern toolchain usage, automated tests, async API handling, or advanced accessibility practices.
UI Component Architecture
1/10
How interface parts are built
No component architecture or modular boundaries; single-file HTML with inline styles and global-scope scripts.
Evidence
Responsive & Cross-browser
2/10
Works on all screens and browsers
Basic responsive touches exist (meta viewport, max-width) but no deliberate responsive system, breakpoints, or cross-browser feature detection.
Performance Optimization
1/10
Speed of the interface
No measurable or deliberate performance work; static pages with inline assets and no lazy loading, code-splitting, or bundle analysis.
Accessibility & Semantics
2/10
Usable for everyone
Some basic semantic use of native controls and image alt text, but no ARIA, focus management, keyboard support for custom widgets, or a11y tooling evident.
State Management & Data Flow
1/10
Managing data in the app
Very simple client-side state held in a global object and synchronous DOM updates; no async state discipline, cancellation, optimistic updates, or validation flows.
UX & Visual Polish
3/10
Look and feel quality
Clean basic visual layout and straightforward user feedback for invalid selection; shows attention to simple UX but lacks richer patterns like skeletons, transitions, or undo.
Technologies
Frontend
UI
GitHub• mentioned only
Recommendations
- Build small interactive SPAs that fetch real APIs (for example convert the weather demo to fetch an actual weather API) and implement proper loading, error states, and request cancellation.
- Refactor UI into modular components and document them in Storybook or a small component library to demonstrate component architecture and reusability.
- Improve accessibility by adding labels, ARIA where needed, keyboard navigation, and integrating an a11y linter or axe checks into CI.
- Adopt a minimal build toolchain (Vite) and add basic testing (Jest or Playwright) plus a simple CI workflow to show modern frontend engineering practices.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: