Data Engineering at Deloitte
Roles, Comp & Culture
An L5 senior data engineer at Deloitte sits around $217K total comp from 130 verified salary datapoints. The primary Data Engineering tech consists of Azure, AWS and GCP, according to current job listings. Deloitte pays data engineers above other Consulting companies. Reviews put them at 3.8 on Glassdoor, a little above the middle of the pack. Employee sentiment at Deloitte reads neutral and employee happiness is trending up over the past year. Layoff risk scores low for the next 30 days. 83 data engineering roles are open right now.
Deloitte data engineer compensation
Each level's figure is the median of individual Deloitte 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.
Deloitte 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.
Deloitte data engineering tech stack
The languages, storage, and processing tools Deloitte data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
SQL
Data FactoryDeloitte data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Develop and oversee data pipelines, model training workflows, and production-grade application components that support AI-enabled products.
Architect, build, and operate scalable batch and near-real-time data pipelines on AWS.
Design and configuration of Microsoft Fabric components (Data Factory, Lakehouse, Warehouses, Pipelines, Semantic Models) and/or Databricks (Delta Lake, notebooks, workflows, ML pipelines)
Design, develop and optimize ETL/ELT pipelines using Azure Data Factory (ADF) and Databricks
Commercial experience in information and data management, with familiarity in enterprise-grade data architecture technologies such as Cloudera CDP, Azure Databricks, and cloud-native environments
Build and enhance data pipelines on AWS using Python to ingest, transform, and deliver data to Snowflake and downstream consumers.
Als (Junior) DevOps Data Engineer ben jij de drijvende kracht achter het beheer, onderhoud en continue verbeteringen van systemen en platformen voor onze klanten.
Als (Senior) DevOps Data Engineer ben jij de drijvende kracht achter het beheer, onderhoud en continue verbeteringen van systemen en platformen voor onze klanten.
Develop and maintain data pipelines, model training workflows, and production-grade application components that support AI-enabled products
As a Senior Consultant - Databricks Engineer in our AI & Data practice, you will design, build, and optimize cloud-based data engineering solutions that support large-scale transformation.
Analyze incidents, troubleshoot production issues, and drive timely resolution to ensure application stability and business continuity.
Practice for the Deloitte loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
Preparing for the Deloitte loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Compare Deloitte with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Deloitte 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