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
Experiences
Practice
Mock InterviewsTimed interview simulations with feedbackCoaching1:1 coaching with people at your target companiesSuccess 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
Build my plan

Diagnose Source vs Pipeline Issues

MediumPipelines00:00
Practice interviewer
In session
5 left
00:00

Your question is Diagnose Source vs Pipeline Issues. 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

Scenario

You are supporting a customer-facing data pipeline and need to determine why the data in the destination looks wrong. The issue could come from the source system, or it could be introduced during ingestion, transformation, or loading.

Question

How would you investigate whether a customer's data issue is caused by bad source data or a platform problem?

What to Inspect

  • Source extract payloads and timestamps
  • Raw landing data before transformation
  • Transformation logic and schema mappings
  • Load history, retries, and duplicate handling
  • Row-count and checksum reconciliation by run

Goal

Key distinction·Bad source data versus platform-introduced corruptionExpected outcome·A repeatable investigation path with evidencePrimary objective·Isolate the first stage where data diverges