Dataford
Interview QuestionsInterview GuidesExperiencesCoaching
New
Pricing
Get started
Dataford
Popular roles
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Browse
Browse All RolesEvery role hub, from analyst to MLBrowse All CompaniesCompany-specific interview loopsAll Interview GuidesThe full guide library
Top questions by role
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Top questions by skill
SQLPythonStatisticsMachine LearningA/B TestingSystem DesignGenerative AIProduct SenseMetricsBehavioral
Browse all questions →Try a mock interview
ExperiencesCoaching
New
Practice
Mock InterviewsTimed interview simulations with feedbackSuccess PathYour 6-week structured planModulesCurated lessons by topicWebinarsTalks from ex-Big Tech data leadsPlaygroundA free-form scratch editor
Learn
BlogInterview strategy and career adviceTech Job Market ReportHiring trends across data and AI rolesFor UniversitiesDataford for career centersAbout DatafordWho we are and how we build
Pricing
Get started

Company-wide Dashboarding System

HardSystem Design00:00
Practice interviewer
In session
5 left
00:00

Your question is Company-wide Dashboarding System. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

You need to log in / sign up to chat or submit.

Problem

Design a company-wide dashboarding system with site-level and master views.

Asked in the round 5 stage as a verbal HLD question. The candidate used an Amazon FCs and scanners analogy, and discussed scanner failures and partial data.

Explain how data is ingested, validated, aggregated, stored, and served to both site-level and master views. Address how the system identifies stale or anomalous data, represents partial results, and prevents one failing site or scanner from corrupting company-wide metrics. Clarify requirements before proposing the design, and discuss online versus batch processing, consistency, observability, and recovery.