Data Engineer Interview Prep & Rubrics

Pipelines, Spark and warehousing, SQL depth, and the modelling and orchestration questions specific to data platforms.

The loop, in order

1. 1Data & AI Engineering40 questions · 8 topicsData engineering, ML engineering and LLM/RAG interview questions covering pipelines, model evaluation, overfitting, retrieval, embeddings, vector search and production AI systems.
2. 2MLOps & ML Platform24 questions · 8 topicsEverything between a model that works in a notebook and one a business depends on: reproducibility, feature and training infrastructure, registries, serving, drift, cost and governance.
3. 3Databases & SQL28 questions · 8 topicsQuery-writing rounds and the storage-engine reasoning behind why a query is slow, for backend, data, and DBA roles alike.
4. 4Python22 questions · 7 topicsPython for backend, automation, and data roles, including the runtime behaviour that surprises candidates who only know the syntax.
5. 5DevOps & Cloud37 questions · 8 topicsPlatform and infrastructure questions for DevOps, SRE, and any backend role expected to own its service in production.
6. 6Behavioural & Culture Fit25 questions · 8 topicsThe round candidates most often under-prepare, treated as a skill with structure rather than small talk, including the rubric interviewers score against.
7. 7HR, Screening & Offer19 questions · 6 topicsThe recruiter and HR rounds that gate the technical loop and decide your compensation, where most candidates improvise and lose money.
8. 8Banking & Financial Services27 questions · 11 topicsHow a bank works underneath the app: the ledger that must always balance, the rails money moves over, the identity and financial-crime controls wrapped around it, and the regulatory reporting that constrains every design decision.
9. 9E-commerce & Retail23 questions · 9 topicsThe path from browse to delivered and back again, where inventory is the central consistency problem and every edge case is a real order belonging to a real person.