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

Data & AI Engineering37 questions · 8 topicsData engineering, ML engineering and LLM/RAG interview questions covering pipelines, model evaluation, overfitting, retrieval, embeddings, vector search and production AI systems.
MLOps & 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.
Databases & SQL25 questions · 8 topicsQuery-writing rounds and the storage-engine reasoning behind why a query is slow, for backend, data, and DBA roles alike.
Python20 questions · 7 topicsPython for backend, automation, and data roles, including the runtime behaviour that surprises candidates who only know the syntax.
DevOps & Cloud37 questions · 8 topicsPlatform and infrastructure questions for DevOps, SRE, and any backend role expected to own its service in production.
Behavioural & Culture Fit21 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.
HR, Screening & Offer18 questions · 6 topicsThe recruiter and HR rounds that gate the technical loop and decide your compensation, where most candidates improvise and lose money.
Banking & 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.
E-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.

Latest for this track

hardConceptScenario

An asyncio call times out and you handle the TimeoutError, but the background task keeps running and mutates shared state a few seconds later. What happened, and how do you make the timeout actually stop the work?

A timeout requests cancellation; it does not stop code. CancelledError is raised at the task next await point, so a task inside blocking code, shielded, in an executor thread, or swallowing the exception keeps running and commits its side effects late. Fix by re-raising CancelledError, keeping blocking calls off the loop, and using asyncio.timeout with a TaskGroup.

Concurrency In Python4 minmid, senior, staff

hardScenarioDesignConcept

A transform has been writing wrong revenue figures for three days and six downstream tables have consumed it. How do you backfill the corrected data without double-counting anything?

Make the write idempotent before you make it correct: partition-level replace rather than append, so rerunning a day produces the same result whether it runs once or five times. Then work out how far the contamination spread, because a downstream table that accumulated rather than recomputed will not fix itself when the source is repaired.

Data Pipelines4 minmid, senior, staff

mediumConceptScenarioCase Study

A churn model scores 0.94 AUC offline and barely beats the baseline in production. One of its strongest features is populated by a process that runs after the customer has already churned. Explain what happened and how you would fix it.

The feature encodes the outcome, so the model reads the answer rather than predicting it. Offline the label and the feature come from the same snapshot, which is why validation looks excellent; in production the feature is empty at scoring time and the signal vanishes. Fix by defining features by when they were available and validating on a forward time split.

Ml Fundamentals5 minmid, senior, staff

hardConceptScenarioDesign

Your error budget burn alert pages every few hours, but half the time nobody outside the team has noticed anything. How do you tune it without simply making it quieter?

A single threshold on a single window cannot separate a real outage from a brief spike, so it either pages on noise or misses slow burns. Replace it with multi-window multi-burn-rate alerting: a fast rule that pages and a slow rule that files a ticket, each gated by a short window so a spike that already stopped does not keep paging.

Sre And Reliability5 minmid, senior, staff

hardConceptScenarioDesign

A table has forty million rows, thirty-five million of them soft-deleted, and every query for active rows has got slower. Would a partial index help, and what would you have to be careful about?

A partial index only contains rows matching its predicate, so indexing WHERE deleted_at IS NULL stores five million entries instead of forty million - smaller, shallower, and far likelier to stay cached. The catch is the planner only uses it when it can prove the query predicate implies the index predicate, and it does nothing about dead rows still in the heap.

Indexing5 minmid, senior, staff

hardScenarioConceptDesign

Pods are being evicted during node pressure even though your CPU dashboards look fine. Where do you look, and what would you change so it stops?

CPU is compressible, so the kubelet throttles it rather than evicting for it. Eviction comes from memory, ephemeral storage or inodes - and which pods die is decided by QoS class, not by who caused the pressure. Check node conditions and eviction thresholds first, then fix the requests and limits so critical workloads are Guaranteed rather than BestEffort.

Kubernetes4 minmid, senior, staff