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.
The trade at Deloitte is exposure for stability. Pay lands above relative to other Consulting companies, and the bench of client work gives you a CV that reads well externally. What you give up is ownership: pipelines you build belong to clients, and the most interesting problems often end when the engagement does. Glassdoor sits at 3.8, a little above the middle of the pack, and Blind sentiment is mixed, which tracks with the consulting model's well-documented tension between bursty project demands and personal sustainability. Happiness is neutral and trending up, suggesting recent conditions have been improving. That upward trend is worth weighing against the structural reality that utilization pressure and client deadlines are features of consulting, not temporary conditions a new manager can fix.
Deloitte's data engineering hiring is expansive right now: 83 open data engineering roles across 47 cities, with Breda among the most active markets. That volume reflects sustained client demand for cloud migration and modern data stack work across multiple geographies, not a sudden reorganization or growth spike in a single practice. The low layoff risk over the next 30 days is consistent with a firm that adjusts headcount through attrition and utilization management rather than mass events. Engineers joining now are likely to land on cloud modernization or AI-adjacent data infrastructure projects, given where consulting budgets are concentrating in 2026. The multi-region hiring pattern also suggests clients are pulling demand across European and North American markets simultaneously.
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.
Deloitte's data engineering work is shaped by client delivery, which means the problems you solve belong to dozens of industries at once: financial services firms needing real-time risk pipelines, healthcare clients wrestling with HL7 and FHIR ingestion, retailers rebuilding their warehouse around a lakehouse architecture. The stack reflects that breadth. Azure, AWS and GCP appear across client engagements, and SQL, Python and PySpark are the daily instruments. You're rarely building for one product with stable SLAs; you're scoping, shipping, and handing off, then doing it again for a different client with different constraints. That cycle suits engineers who want variety across data domains more than it suits engineers who want to go deep on one proprietary platform.
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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.
Deloitte hires across all levels, but the salary pool is heaviest at junior and mid: 59 reports at junior and 52 at mid versus 21 at senior. That means the firm is a genuine entry point for data engineers early in their careers, and the multi-cloud exposure across Azure, AWS and GCP builds marketable breadth fast. Senior engineers who want to own a data platform end-to-end, set long-term architecture direction, or specialize deeply in streaming will likely find the client rotation model limiting. The interview loop emphasizes pipeline architecture, with a Python screen, so prep for design questions that span ingestion through serving. If the model fits, check the compensation ladder and the open roles before reaching out to a recruiter.
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