McKinsey & Company Data Engineer Interview Guide
The McKinsey & Company data engineer loop, round by round: what each stage tests, example questions with the guidance interviewers actually score, the mistakes that sink strong candidates, and how to prepare.
10 real McKinsey & Company interview questions
Reported by candidates from real loops, tagged by domain, round, level, and year. Expand for what the round is scoring.
SQLL3 · 2021Join on the keys that are not equal but kind of similarBehavioral+
SQLL5 · 2021sq:l moving averagePhone screen · screen sql+
SQLL4Given a table of pizza toppings and costs, output all unique 3-topping combinations with their total cost; toppings within each pizza must be listed alphabetically joined by commas; sort by descending total cost then ascending pizza name; schema: pizza_toppings(topping_name VARCHAR, ingredient_cost DECIMAL)Online assessment+
SQLL5Technical round-Questions on SQLTechnical · screen+
PythonL3 · 2023Find the number of permutations in a certain infection problem.Online assessment+
PythonL5 · 2022Number of Pythagorean triplesPhone screen · screen sql+
PythonL5 · 2022Question similar to minimum number of boxesPhone screen · screen sql+
PythonL5 · 2021python:dp activate fountainPhone screen · screen sql+
PythonL4Write a function that takes a dictionary and returns a sorted list of values that occur exactly once in the dictionaryOnline assessment+
PythonL5asked to do some operations using python-pandasTechnical · screen+
How candidates rate the McKinsey & Company loop
How hard candidates rated the loop and how they felt, summarized across the reports below.
12 rated reports
11 rated McKinsey & Company reports
Try a McKinsey & Company-style SQL round
Find every user active on 3 or more CONSECUTIVE days. This gaps-and-islands shape shows up in nearly every DE SQL round. Edit the query and run it against the seed data.
McKinsey & Company is hiring data engineers now
The roles behind this loop. Prep against the levels and locations they are actually filling.
You will lead the development of robust data pipelines, manage secure and governed data environments, and mentor junior colleagues while collaborating with clients and cross-functional teams.
You will design and maintain scalable data pipelines, manage secure data environments, and prepare data for AI-driven systems while collaborating with cross-functional teams and clients.
In this role you will design, develop, and maintain scalable data pipelines and systems using Databricks on Azure (among other environments).
You will work in a team of data engineers to develop data ingestion pipelines, create and mature data processing capabilities that ingest data into a data system used by GenAI applications.
Our Source AI team uses generative AI to analyze vast amounts of data, identify patterns, and create actionable insights within Procurement.
McKinsey & Company compensation and culture
The numbers, tech stack, and team structure live on the company overview.
Compare McKinsey & Company with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at McKinsey & Company interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition