Anghami Software Engineer Interview
Applied backend problem-solving (Go/PHP), streaming-scale systems and data/ML, and a deep project walk-through.
Start the Anghami mock interview → See all boards
A free AI panel interview that questions you in Anghami’s style, follows up when an answer is thin, and returns a scored, evidence-backed report — seeded from your own practice history on Wrexa Edge.
The Anghami interview, in brief
Anghami is the MENA region's leading music-streaming platform (HQ Abu Dhabi, major engineering hub in Beirut), NASDAQ-listed, and operator of the video platform OSN+. Engineering spans backend services, large-scale data pipelines and ML-driven recommendations over a 30M+ song catalog. Its stack is well-documented via a public engineering blog: Go (with PHP for legacy), Python/Spark for data and ML, and AWS-heavy infrastructure (DynamoDB, Redis, Redshift, S3).
Anghami's exact round structure is lightly documented (a small number of public reviews); the high-level funnel is a recruiter/HR screen and one or more technical interviews, with a behavioural/hiring-manager fit round inferred. The technical stage is distinctive: candidates explain prior work in detail and are given scenarios to solve over their real stack (including Kubernetes/infra), rather than an abstract-algorithm gauntlet. Coding depth is track-dependent (backend, data, ML). Interviewers are described as fair and professional; the process runs roughly two to four weeks.
Interview rounds
-
1. Recruiter / HR screen Non-technical
-
2. Technical interview(s) Applied / scenario-based
-
3. Behavioural / hiring-manager fit Inferred
Coding & DSA topics
System design
Grounded in their documented stack and domain: catalog/metadata modelling for a 30M+ song library, recommendation/personalization pipelines (candidate-generation → scoring → re-ranking, their "UserDNA" re-ranker), event ingestion at billions of streams, and low-latency serving via DynamoDB and Redis, plus playlists/search and (on the OSN+ side) video. LLD tends to fold into the scenario technical round; a formal standalone whiteboard HLD round is not explicitly documented.
Behavioural round
No formal published values page surfaced. Culture signals from reviews and the engineering blog: fast-moving, product-led, strong data-democratization culture, and pride in being a regional MENA engineering success story. Behavioural focus is genuine motivation to join Anghami plus detailed ownership of past work.
Representative question categories
Patterns in Anghami’s style — not verbatim proprietary questions.
- Project deep-dive — Walk through a system you built — decisions, trade-offs and metrics.
- Motivation & fit — Why Anghami, and what do you know about the product and market?
- Applied backend — Solve a backend scenario over Go/PHP, APIs and microservices.
- Infrastructure — Kubernetes, deployment and scaling reasoning.
- Data & ML — Design a data pipeline or a recommendation flow (data/ML tracks).
- Streaming design — Design catalog/serving for a 30M-song library at streaming scale.
How to prepare
Do
- Read their engineering blog (talks.anghami.com) — the strongest public signal of what they value; reference their real stack (Go, Spark, DynamoDB, recommendations).
- Prepare to narrate past work in depth with concrete metrics and decisions.
- Have a specific, genuine reason for Anghami and MENA music-tech.
- Brush up practical infrastructure (Kubernetes, caching, database trade-offs), not just abstract DSA.
Avoid
- Over-indexing on hard LeetCode while under-preparing project narration and applied scenarios.
- Generic "Why Anghami?" answers lacking product or market awareness.
- Assuming the wrong stack — the documented backend is Go/PHP with Python/Spark for data/ML, not Node-only.
- Ignoring streaming-domain concerns (scale, caching, recommendations) in design discussion.
Who can apply
Roles run from intern to senior across Backend, Android, ML, Data and InfoSec, primarily in Beirut and Abu Dhabi (also Cairo). No public leveling ladder is documented.
Anghami interview FAQ
- What tech stack does Anghami use?
- Per their public engineering blog, the backend is Go (with PHP for legacy systems), data and ML use Python and Spark, and infrastructure is AWS-heavy: DynamoDB as the primary store, Redis for low-latency serving, plus Redshift and S3. It is not a Node-only backend.
- Is Anghami's interview a hard-LeetCode gauntlet?
- No — reports describe an applied, scenario-based technical round over their real stack (including infrastructure), plus a detailed walk-through of your past work, rather than an abstract-algorithm gauntlet.
- What system design does Anghami ask?
- Streaming-domain design: catalog/metadata for a 30M+ song library, recommendation pipelines (candidate-generation → scoring → re-ranking), event ingestion at scale, and low-latency serving via DynamoDB and Redis.
- How should I prepare for Anghami?
- Read their engineering blog (talks.anghami.com), prepare to narrate a past project in depth with metrics, brush up practical infrastructure (Kubernetes, caching, DB trade-offs), and have a genuine reason for Anghami and MENA music-tech.
Practise now
Rehearse the full loop with an AI panel modelled on Anghami’s process, in text, voice, or video.
Other company interviews
Verified from
- Anghami — Careers (Breezy)
- Anghami Engineering blog — Crunching Data at Anghami
- Glassdoor — Anghami interview questions
Wrexa Edge is an independent exam-prep platform and is not affiliated with, authorised by, or endorsed by Anghami. This guide is based on publicly documented interview practice; it does not reproduce any employer’s internal rubric or question bank, and it does not predict a hiring outcome.