These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Preparation for AMD requires a shift in mindset. While technical competence is non-negotiable, interviewers here are deeply interested in your practical experience and how you apply your skills to solve real business problems. You should treat your resume as the primary agenda for your interviews; expect to defend every tool and project listed on it.

To succeed, focus on demonstrating strength in these key evaluation criteria:

Role-Related Technical Proficiency – 2–3 sentences describing: AMD evaluates your hands-on capability with the modern data stack, specifically SQL, Python, and Spark. You need to demonstrate not just that you can write code, but that you understand the underlying mechanics, such as memory allocation in PySpark or optimization in Snowflake/Databricks.

Resume and Project Depth – 2–3 sentences describing: Unlike companies that rely solely on LeetCode-style puzzles, AMD interviewers often use your resume as a roadmap. You must be able to articulate the "why" and "how" of your past projects, explaining architectural decisions and the specific impact of your contributions.

Problem-Solving in Ambiguity – 2–3 sentences describing: You will face scenarios where requirements are vague or data is messy. Interviewers look for candidates who can ask the right clarifying questions, identify environment variables, and propose scalable solutions without needing their hand held.

Collaboration and Communication – 2–3 sentences describing: Data Engineers at AMD frequently interact with non-technical stakeholders and hardware engineers. You are evaluated on your ability to translate complex technical concepts into clear insights and your willingness to work as part of a cohesive panel or team.

The interview process for Data Engineers at AMD is generally streamlined and practical. It typically begins with a recruiter screen, followed by a technical screen or a hiring manager interview. If you pass these initial checks, you will move to a final round. Unlike the grueling, day-long loops common at some software giants, AMD’s final stage is often more compact, sometimes consisting of a panel interview lasting 45 to 60 minutes, or a series of two to three shorter 1:1 interviews.

Expect a process that feels "medium" in difficulty but high in specificity. The interviews are less about trick questions and more about validating the skills you claim to have. You will face a mix of behavioral questions, resume deep-dives, and technical concept checks. The atmosphere is generally professional and collaborative, though some candidates report that interviewers will probe deeply to ensure you aren't guessing at answers.

This timeline illustrates a typical progression from application to offer. Note that the "Panel/Final Round" is often the decisive moment where technical fit and cultural alignment are assessed simultaneously. Use the time between the technical screen and the panel to review the specific technologies mentioned in the job description, as the panel will likely quiz you on them directly.

Based on recent candidate data, the evaluation for this role focuses heavily on three core pillars: SQL fluency, Big Data processing (specifically Spark/Python), and resume validation. The questions are designed to expose whether your knowledge is theoretical or born from battle-tested experience.

SQL and Data Warehousing

This is the bread and butter of the interview. You must demonstrate advanced SQL capabilities, as you will likely be manipulating large datasets within platforms like Snowflake or Databricks.

Be ready to go over:

• Complex Queries – Writing and optimizing queries using Common Table Expressions (CTEs) and Joins.
• Window Functions – Understanding how to perform calculations across a set of table rows (e.g., RANK, LEAD, LAG) is frequently tested.
• Database Fundamentals – Indexing strategies, normalization vs. denormalization, and query performance tuning.

Example questions or scenarios:

• "Write a query to find the top 3 salaries in each department using a Window function."
• "How would you optimize a query that is performing poorly on a large dataset in Snowflake?"
• "Explain the difference between a CTE and a temporary table, and when you would use each."

Python and Big Data Frameworks (Spark)

AMD relies heavily on Python and Spark for data transformation. Interviewers will test your understanding of how these tools work under the hood, not just syntax.

Be ready to go over:

• PySpark Internals – Understanding system allocation, how Spark handles memory, and the difference between transformations and actions.
• Python OOP – Object-Oriented Programming concepts (classes, inheritance) are fair game, especially for building maintainable pipelines.
• Environment Management – Handling environment variables and configuration in a production setting.

Example questions or scenarios:

• "Explain how Spark handles memory management and partition allocation."
• "Design a Python class for a data ingestion pipeline. How would you structure the inheritance?"
• "What are the common pitfalls when working with environment variables in a cloud deployment?"

Resume and Experience Probe

Your past experience is the primary source material for behavioral and situational questions. Interviewers will pick specific projects from your resume and ask you to explain them in granular detail.

Be ready to go over:

• Project Architecture – Drawing out the pipelines you built and explaining why you chose specific tools.
• Challenges Faced – Discussing technical roadblocks and how you overcame them.
• Impact – Quantifying the results of your work (e.g., "reduced latency by 20%").

Example questions or scenarios:

• "Walk me through the most complex pipeline you built in your last role."
• "Tell me about a time you had to learn a new technology quickly to solve a problem."
• "I see you listed [Tool X] on your resume; explain how you used it to solve [Problem Y]."

The word cloud above highlights the most frequently occurring terms in AMD Data Engineer interviews. Notice the prominence of SQL, Projects, Python, and Spark. This indicates that while general CS knowledge is good, your preparation should lean heavily toward data-specific tooling and a deep review of your own project history.