Data Engineering at KPMG
Roles, Comp & Culture
An L7 principal data engineer at KPMG sits around $230K total comp from 29 verified salary datapoints. The primary Data Engineering tech consists of Azure, Databricks and Spark, according to current job listings. KPMG pays data engineers slightly below the other companies we track. Reviews put them at 3.7 on Glassdoor, a little below the middle of the pack. Employee sentiment at KPMG reads neutral and employee happiness has held flat over the past year. Layoff risk scores low for the next 30 days. 31 data engineering roles are open right now.
KPMG data engineer compensation
Each level's figure is the median of individual KPMG 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.
KPMG 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.
KPMG data engineering tech stack
The languages, storage, and processing tools KPMG data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
SQL
Data Factory
FabricKPMG data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Design and implement data ingestion and transformation pipelines using Fabric Data Factory, notebooks, and Spark.
Design and implement scalable batch and streaming pipelines using Spark and modern orchestration patterns.
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
Develop your career through a range of multifaceted engagements, formal training, and informal mentoring.
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)
Experience with data processing / ETL knowledge: Extract, Transform, Load (ETL) processes and tools for data ingestion, integration, transformation
Develop, optimize, and maintain ELT pipelines using Snowflake-native capabilities and modern orchestration tools
Preparing for the KPMG loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Compare KPMG with other data engineering employers
How the role, pay, and loop stack up against peer companies.
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