KPMG Data Engineer Interview Guide

The KPMG 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.

Last updated: July 31, 2026Proudly published by: Jeff Wahl

How candidates rate the KPMG loop

How hard candidates rated the loop and how they felt, summarized across the reports below.

How hard candidates rated it

Easy

50%

Medium

33%

Hard

17%

6 rated reports

How candidates felt

Positive

83%

Neutral

17%

Negative

6 rated KPMG reports

Try a KPMG-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.

	/* Users active on 3+ consecutive days. */
	/* Hint: date minus a per-user ROW_NUMBER is constant within a streak. */
	WITH streaks AS (
	SELECT
	user_id,
	activity_date,
	activity_date - CAST(
	(ROW_NUMBER() OVER (
	PARTITION BY user_id
	ORDER BY activity_date
	))
	AS INT
	) AS grp
	FROM user_sessions
	)

	SELECT
	user_id
	FROM streaks
	GROUP BY user_id, grp
	HAVING COUNT(*) >= 3

KPMG is hiring data engineers now

The roles behind this loop. Prep against the levels and locations they are actually filling.

Hiring now

KPMG data engineer · live from career pages

open roles

New York

top metro

Design and implement data ingestion and transformation pipelines using Fabric Data Factory, notebooks, and Spark.

L5Toronto28d ago

Design and implement scalable batch and streaming pipelines using Spark and modern orchestration patterns.

L5Calgary28d ago

Support the development of Retrieval-Augmented Generation (RAG) and context engineering pipelines from audit knowledge sources and the integration into AI agent workflows; design and implement the use of metadata across knowledge systems to drive the use of context

L4New York53d ago

Develop your career through a range of multifaceted engagements, formal training, and informal mentoring.

L5Chicago58d ago

Assist with technical design and development activities and lead a small workstream for implementation of large-scale data solutions in Databricks to support multiple use cases (delta lake, reporting and analytics, AI/ML)

L5Santa Clara72d ago

Experience with data processing / ETL knowledge: Extract, Transform, Load (ETL) processes and tools for data ingestion, integration, transformation

L5Washington74d ago

Develop, optimize, and maintain ELT pipelines using Snowflake-native capabilities and modern orchestration tools

L4Irvine107d ago

New postings per week

3/2

4/13

5/18

6/1

6/8

6/29

week beginning · ~17 weeks of data

Where they hire

New York

Washington DC

Chicago

Toronto

Levels hiring

L42L55

Updated Jul 31, 202631 open listings across 6 cities

KPMG compensation and culture

The numbers, tech stack, and team structure live on the company overview.

Data engineering at KPMG→

The role, comp by level, tech stack, teams, and culture signals.

Compare KPMG with other data engineering employers

How the role, pay, and loop stack up against peer companies.

Data engineering at Airbnb→

Compare the role, comp, and interview loop.

Data engineering at Amazon→

Compare the role, comp, and interview loop.

Data engineering at Apple→

Compare the role, comp, and interview loop.

Data engineering at Databricks→

Compare the role, comp, and interview loop.

02 / Why practice

Prepare at KPMG 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

Practice KPMG-level SQL

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