Deloitte Data Engineer Interview Guide
The Deloitte 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.
What the Deloitte loop tests: domains and difficulty
Our prediction of the question mix by domain and difficulty for this company's data engineer loop, from live listings and interview reports.
The technical bar centers on pipeline architecture, with a Python screen as the gate into the full loop. SQL questions are the most reported domain, and at Deloitte that tends to mean multi-step analytical SQL rather than trivia: window functions, aggregation over partitions, and queries that reflect real reporting patterns. Because Azure, AWS and GCP span all 3 major clouds and SQL, Python and PySpark include PySpark, the loop doesn't assume a single platform; expect to justify tool choices, especially when a cheaper or simpler option exists. A passing answer at Deloitte explains the why: why this orchestration pattern over another, why this partitioning strategy given the client's query profile. Generic answers that could apply anywhere read as insufficient here.
The domain and difficulty mix we predict for a Deloitte data engineer loop, across 12 problems. It updates as more Deloitte data lands.
6 real Deloitte interview questions
Reported by candidates from real loops, tagged by domain, round, level, and year. Expand for what the round is scoring.
SQLL5 · 2025How would you compute rolling metrics, such as a 7-day moving average, on large event tables?Onsite · sql+
SQL window function question from Deloitte Data Engineer technical round. Expected approach: use AVG() OVER(PARTITION BY entity ORDER BY event_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) for row-based 7-day moving average, or RANGE BETWEEN INTERVAL 6 days PRECEDING AND CURRENT ROW for calendar-based. Discussion expected on performance at scale: partitioning strategy for large event tables, potential use of materialized views for pre-computation, and validation approaches to ensure correctness of rolling calculations.
SQLL3 · 2025How would you calculate daily active users and weekly active users from an events table?Phone screen · screen sql+
Deloitte Data Engineer first technical interview question. Given an events table with columns (user_id, event_type, event_timestamp), define and compute DAU and WAU metrics. Expected approach: COUNT(DISTINCT user_id) grouped by DATE(event_timestamp) for DAU, and COUNT(DISTINCT user_id) grouped by DATE_TRUNC(week, event_timestamp) for WAU. Discussion points: handling timezone differences in event timestamps, whether bots/automated events should be excluded, how to define "active" (any event vs specific engagement events), and how to efficiently compute these metrics on tables with billions of…
SQLL3Find employees who earn more than their direct manager; return employee_id and employee_name; schema: employee(employee_id, name, salary, department_id, manager_id)Onsite · sql+
Data modelingL5 · 2025How would you identify duplicate records in a dataset and keep only the most recent entry per key?Onsite · data modeling+
Deloitte Data Engineer data modeling/quality question. Candidate expected to discuss: defining what constitutes a duplicate (exact match vs fuzzy), choosing a deduplication key, using ROW_NUMBER() PARTITION BY key ORDER BY updated_at DESC to rank records and keep rank=1, handling ties in timestamp, and designing the pipeline to prevent future duplicates via unique constraints or merge/upsert patterns. Touches both data modeling strategy and SQL implementation.
System designL6 · 2025How would you add a column to a billion-row table without affecting user experience?Onsite · pipeline architecture+
Deloitte Data Engineer system design question from the technical interview round. Candidate expected to discuss online schema change strategies: shadow table approach (create new table with column, backfill, rename), using pt-online-schema-change or gh-ost for MySQL, or ALTER TABLE... ADD COLUMN with DEFAULT for Postgres (instant in PG11+). Must address: zero-downtime migration, backfill strategy for existing rows, application-level backward compatibility during rollout, index considerations, and rollback plan. Source provided the question but limited follow-up detail.
System designL7 · 2024Design a pipeline that ingests images and PDFs of resumes and transforms them into queryable text dataOnsite · pipeline architecture+
End-to-end system: document ingestion (S3/blob), OCR extraction (Textract/Tesseract), text parsing and structuring, loading into a queryable store (Elasticsearch or SQL), with error handling and retry logic.
Offers tend to slip at the pipeline architecture stage, where otherwise-qualified candidates over-engineer for a consulting context. Deloitte's projects ship and hand off; a design that assumes 18 months of iteration and a dedicated ops team won't land the same way it might at a product company. Candidates who fail usually present architectures that are impressive in isolation but ignore operability by the client post-engagement. On the other side, candidates who pass show they can make pragmatic calls quickly and articulate the constraints they were optimizing for. Interviewers are also listening for how you handle scope creep in your examples: if every project story ends cleanly with no trade-offs acknowledged, that reads as a yellow flag in a consulting loop where clean endings are rare.
Try a Deloitte-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.
Practice the Deloitte loop
The problems our model expects in this company's interview, grouped by round. Work the shapes that come up, not the ones that read well on a list.
Deloitte's loop is filtering for client-ready engineers, and that shapes every round. The firm's data engineering work lives inside engagements where requirements shift mid-project, stakeholders are non-technical, and the deliverable has to be handed off to a client team that may not have senior DE support after you leave. So what interviewers are measuring beyond technical correctness is whether you can scope work under ambiguity, communicate trade-offs to people who don't read query plans, and build something another team can maintain without you. Engineers who treat pipeline design as a purely technical exercise tend to stall here; the ones who pass have internalized that the client's constraints are part of the design input, not a nuisance to route around.
Deloitte is hiring data engineers now
The roles behind this loop. Prep against the levels and locations they are actually filling.
Allocate the first block of prep to SQL and pipeline architecture in that order. SQL is the highest-reported domain and the screen will likely filter on Python, so get those right before anything else. For architecture prep, practice designing pipelines with explicit hand-off and operability constraints: what does this look like for a client team of 2 who inherit it in 3 months? That framing will differentiate your answers. At senior and above, the bar shifts toward justifying decisions under competing constraints and scoping ambiguous asks, so senior candidates should spend more time on communication structure in design rounds. The compensation ladder here runs 3 levels; if leveling is a negotiation point, 132 reported offers give you a real reference range to anchor on going in.
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.
Deloitte compensation and culture
The numbers, tech stack, and team structure live on the company overview.
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