TaqTak
Static LeetCode-style puzzles and silent take-home assignments don't reflect how engineers actually work, they tell you if a candidate memorized an algorithm, but they miss the most important stuff: how they reason through an unfamiliar bug, communicate under pressure, or collaborate when requirements shift.
As the lead Backend Engineer at Algebra Intelligence, I’ve been building TaqTak to fix that.
TaqTak is designed to make technical hiring practical and interactive, instead of sending candidates off into a vacuum, interviewers host structured sessions with integrated video, dynamic coding challenges, and live candidate observation.
Here is a look at what went into building the backend, the engineering trade-offs involved, and what I learned along the way.
The Backend & Architecture Behind TaqTak
Behind the scenes, TaqTak relies on a clean, scalable backend architecture built with Python, Django, and Django REST Framework, using PostgreSQL (via the Django ORM) for persistent data storage.
Rather than complicating the infrastructure with persistent WebSocket connections, the core engine runs on high-throughput, highly optimized RESTful APIs and asynchronous background tasks,his keeps the backend lean, predictable, and easy to maintain while reliably serving session orchestration, challenge delivery, and candidate assessment workflows.
The Toughest Engineering Challenges
Even with standard HTTP workflows, managing live, multi-participant assessment sessions introduces unique engineering complexity:
1. Reliable Session State & Lifecycle Management
An interview session involves multiple moving parts, interviewers updating problem prompts, candidates running code, and notes being recorded simultaneously, because every action goes through stateless HTTP endpoints, ensuring consistency across participants without race conditions required strict database transaction handling, careful lock strategies, and clean API design.
2. Performance & Database Optimization
As user traffic grows, database bottlenecks quickly become the primary point of failure, I spent significant effort optimizing Django ORM queries, eliminating $N+1$ query issues using select_related and prefetch_related, caching hot data, and indexing frequently queried session and challenge tables to keep response times sub-hundred-millisecond under load.
3. Granular Security & Data Isolation
TaqTak handles sensitive data, proprietary interview question banks, candidate evaluations, personally identifiable information, and private code submissions, security couldn't be an afterthought patched on at the end, I built custom permission classes and multi-layered authorization directly into Django's authentication pipeline so that live session access, evaluation records, and candidate profiles remain strictly isolated and protected.
What Building TaqTak Taught Me
When you’re moving fast to ship product features, it’s easy to over-engineer solutions for problems you don't actually have yet, or worse, rush out messy code that makes future updates a nightmare.
Working on TaqTak reinforced a key lesson: Great backend engineering isn't about building the most complex system possible; it's about building an architecture simple enough for today, but flexible enough to evolve tomorrow, choosing a robust, stateless REST setup over complex real-time machinery kept our development velocity high without sacrificing reliability.
Building TaqTak has been one of the most rewarding engineering challenges of my career, we’re continuing to iterate on the platform, and I'm excited to share more technical deep dives as we grow.