Data Engineering at McKinsey & Company
Roles, Comp & Culture
An L6 staff data engineer at McKinsey & Company sits around $245K total comp from 60 verified salary datapoints. The primary Data Engineering tech consists of CI/CD, Azure and Snowflake, according to current job listings. McKinsey & Company pays data engineers in line with other Consulting companies. Reviews put them at 4.1 on Glassdoor, among the highest of any company here. McKinsey & Company employees report healthy morale and employee happiness is trending up over the past year. Layoff risk scores low for the next 30 days. 7 data engineering roles are open right now.
McKinsey & Company data engineer compensation
Each level's figure is the median of individual McKinsey & Company offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.
McKinsey & Company employee sentiment, tracked weekly
Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.
McKinsey & Company data engineering tech stack
The languages, storage, and processing tools McKinsey & Company data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
SQLMcKinsey & Company data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
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.
Preparing for the McKinsey & Company loop
The round-by-round process, example questions, and prep plan are on the interview guide.
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