Preparing for an interview at McKinsey & requires a broader approach than preparing for a standard software engineering loop. You must balance deep technical preparation with intense practice in structured communication and leadership storytelling.

Role-related knowledge – You must demonstrate a deep, conceptual understanding of data engineering fundamentals. Interviewers will push past surface-level definitions to test your knowledge of distributed computing internals, query execution plans, and data storage formats. Be prepared to defend your technical choices, explaining the exact architectural trade-offs you made in your past projects.

Problem-solving ability – McKinsey evaluates this through technical case studies, often referred to as Problem Solving Scenarios (PSS). You are expected to approach ambiguous problems systematically by breaking them down into logical frameworks, asking clarifying questions, and proposing pragmatic, scalable solutions. Your ability to structure your thoughts out loud is just as important as the final architecture you design.

Leadership & Personal Impact – Evaluated through the Personal Experience Interview (PEI), McKinsey & looks for candidates who can take ownership, influence others, and drive initiatives to completion. You must prepare deep, highly detailed behavioral stories that highlight your specific actions, decisions, and interpersonal dynamics, rather than just the collective achievements of your team.

Culture fit & collaboration – As a Data Engineer, you will interact with diverse teams and clients who may not have a technical background. Interviewers will assess your empathy, your ability to translate complex technical concepts into clear business value, and how well you handle the high-pressure, collaborative environment of consulting.

The interview process for a Data Engineer at McKinsey & is rigorous, multi-staged, and designed to evaluate a holistic mix of technical excellence and strategic consulting skills. Candidates can expect a structured journey that typically spans several weeks, involving multiple members of both the engineering and consulting practices.

The process begins with an initial screening phase, which frequently includes a timed technical assessment on platforms like HackerRank or a LeetCode-style take-home assignment focusing on Python algorithms and SQL. This is followed by a conversational recruiter screening to discuss your background, career goals, and alignment with the firm's values.

Once past the initial screens, you will enter the core interview rounds, which are split into technical evaluations, Problem Solving Scenarios (PSS), and Personal Experience Interviews (PEI). These rounds are highly structured and are often conducted in consecutive blocks. You will meet with senior data engineers, technical architects, and management consultants who will evaluate your ability to solve complex data problems on the spot while assessing your leadership potential.

The visual timeline above outlines the typical progression a candidate goes through during the McKinsey & hiring loop. Use this timeline to pace your preparation, ensuring you allocate sufficient time to practice both your live coding skills and your structured case study frameworks before reaching the intensive final rounds.

To excel in the McKinsey & Data Engineer interview, you must understand exactly what the interviewers are looking for in each core competency area.

Python & PySpark Engineering

This evaluation area focuses on your ability to write clean, maintainable, and highly optimized code for processing large-scale datasets. Interviewers want to see that you understand how distributed systems execute your code under the hood.

Be ready to go over:

• Distributed computing concepts – Understanding how Spark manages memory, driver-worker communication, and lazy evaluation.
• DataFrame optimization – Utilizing broadcast joins, avoiding expensive shuffles, and managing partition sizes to prevent skew.
• Data serialization and formats – Choosing and optimizing storage formats like Parquet, Avro, and Delta Lake.
• Advanced concepts (less common) – Custom User Defined Functions (UDFs) optimization, tuning Spark configuration parameters, and writing custom PySpark transformers.

Example scenarios:

• "You have a PySpark job that is failing with an OutOfMemory (OOM) error during a massive join of two datasets. Walk me through your debugging and optimization process."
• "Explain how you would handle data skew in a dataset where 90% of the records share the same partition key."

SQL & Data Modeling

This area tests your ability to query structured databases efficiently and design data warehouses that serve as a single source of truth for complex analytics.

Be ready to go over:

• Analytical SQL – Advanced window functions, complex aggregations, recursive queries, and query execution plan analysis.
• Dimensional modeling – Star schemas, snowflake schemas, fact and dimension table design, and handling historical changes.
• Storage optimization – Indexing strategies, partitioning, clustering, and materialized views.
• Advanced concepts (less common) – Designing data vaults, modeling semi-structured data at scale, and implementing zero-copy cloning in modern data warehouses.

Example scenarios:

• "Design a dimensional data model for a global banking client that needs to track real-time loan applications across multiple branches and currencies."
• "Write a SQL query to identify the top 5% of customers by transaction volume for each month, ensuring the query is optimized for a multi-terabyte dataset."

Problem Solving Scenarios (PSS)

The PSS is a technical case study that mirrors the day-to-day work of a McKinsey & consultant. It evaluates how you approach highly ambiguous, complex technical challenges and structure your recommendations.

Be ready to go over:

• System design & architecture – Designing end-to-end data ingestion, processing, and storage pipelines.
• Trade-off analysis – Evaluating batch vs. real-time processing, open-source vs. managed services, and cost vs. performance.
• Migration strategies – Planning the transition from legacy on-premise systems to modern cloud data platforms.
• Advanced concepts (less common) – Multi-region data replication, disaster recovery planning, and implementing strict data governance and compliance frameworks (GDPR/HIPAA).

Example scenarios:

• "A major healthcare provider wants to build a centralized data platform to analyze patient outcomes in real-time. How would you design this system, and what key technical decisions would you present to the client's CTO?"
• "Walk me through how you would design a data ingestion framework that can scale to ingest data from over 100 different external APIs with varying schemas."

Personal Experience Interview (PEI)

The PEI is McKinsey &'s unique behavioral evaluation. Instead of asking you to list multiple examples quickly, the interviewer will spend almost the entire time diving deep into a single story to understand your exact actions, thoughts, and impact.

Be ready to go over:

• Personal impact – Demonstrating how you persuaded or influenced a group or a key individual to adopt your point of view.
• Entrepreneurial drive – Showing how you took the initiative to solve a major problem, overcome significant obstacles, and deliver outstanding results.
• Inclusive leadership – Explaining how you led a team through a challenging situation, resolved conflicts, and fostered collaboration.
• Advanced concepts (less common) – Navigating highly political client environments, managing up with senior executives, and driving cultural change within engineering organizations.

Example scenarios:

• "Tell me about a time when you had to lead a technical team through a major pivot in project direction. How did you align the team and manage their concerns?"
• "Describe a situation where you had to deliver a critical data engineering project despite facing severe resource constraints and incomplete technical specifications."