Stop grinding leetcode for data engineer interviews!

Landing a role in big tech and “grinding leetcode” have gone together like peanut butter and jelly for the last ten years.
This world is changing rapidly though. Roy Lee created InterviewCoder to cheat on these “leetcode-style” interviews and landed multiple offers from big tech with it. Instead of fighting the trend, Meta announced they will allow candidates to use AI during coding interviews!

This shift is going to make rote memorization less important and companies will need to evaluate debugging skills, critical thinking and creativity more!

In this article we will go over how things are going to change, keeping in mind that companies take time to adapt so you will probably still see “old school” interviews for the next few years!

The six ways that interviews will change

• Grinding Leetcode → Correcting AI-Generated Code

• Sweating Over SQL → Contextual Data Modeling

• Systems Design is Still King

• Random Hadoop Trivia → Data Lake Architectures

• Writing clean code → Product Sense

• A Deeper Focus on Past Projects

  

1. Grinding Leetcode → Correcting AI-Generated Code

In the past, endless Leetcode prep was seen as a rite of passage. Now, companies are realizing that AI can brute-force algorithmic questions faster than humans. What matters is whether you can spot mistakes, fix logic errors, and adapt code to real-world constraints.

• Interviewers may paste AI-generated SQL, Spark, or Python that is subtly wrong.

• Your job: debug it, optimize it, and explain your reasoning.

• This tests critical thinking, not rote memorization.

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2. Sweating Over SQL → Contextual Data Modeling

Knowing SELECT statements cold is table stakes. What’s harder (and more valuable) is understanding the business context and designing schemas that serve it.

• Example: not just writing a join, but deciding whether facts and dimensions are modeled in 3NF, star schema, or a data vault.

• You’ll be asked to evaluate trade-offs: query speed vs storage cost, schema evolution vs governance.

• How do you build RAG systems and contexts for AI systems to make smarter decisions?

• This shift emphasizes product sense + engineering sense, not just syntax.

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3. Systems Design is Still King

Even with AI everywhere, companies still want engineers who can reason at architecture scale.

• Designing Data-Intensive Applications remains the bible for these questions.

• Topics that keep surfacing: data partitioning, streaming vs batch trade-offs, fault tolerance, scaling metadata catalogs, and governance.

• AI might help brainstorm, but you must show you understand distributed systems intuitively.

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4. Random Hadoop Trivia → Data Lake Architectures

Nobody cares if you remember the difference between Pig and Hive anymore. What matters is whether you can navigate modern lakehouse ecosystems.

• Expect questions on Iceberg vs Delta vs Hudi, catalog management (Glue, Unity, etc), and query engines (Trino, Spark, Snowflake).

• Companies want to see if you understand how storage formats, metadata, and compute interact.

• It’s less about “what command” and more about designing a pipeline that can evolve over time.

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5. Writing Clean Code → Product Sense

One thing that engineers have prided themselves in for decades is writing clean and scalable code. Clean code has become commoditized. The real value now is putting clean code in places that provide value.

• Asking clear questions like

  • How does this data provide business value?

  • Should this pipeline even exist?

  • Are there opportunities to consolidate with other definitions?

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6. A Deeper Focus on Past Projects

If you look at interviews for other professions like surgeons, they don’t have the surgeon do “practice surgery” to land the job. They have the surgeon talk about their past experiences and see if it lines up with what they are looking for!

Data engineers should come prepared with stories from their past that

• Demonstrate business impact

  • People hire others that help the business succeed

• Demonstrate technical depth

  • People hire others that are smarter than they are

• Demonstrate good judgement and communication skills

  • People hire others that make smart decisions and are easy to work with

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The Bigger Trend

Interviews are shifting from rote recall → reasoning in context. AI is going to relieve us of having to memorize the coding dictionaries but puts the responsibility on us to use these new found powers responsibly and effectively!

Engineers prove their value by debugging, modeling, and architecting systems with clear business impact that AI can’t fully reason about yet.

By 2030, “grinding Leetcode” will feel as outdated as memorizing punch-card offsets. The differentiator will be whether you can steer AI outputs into production-ready systems.

My five week AI Boot camp starts on October 20th, you can get 30% off with code INTERVIEWAI on DataExpert.io. We will be covering prompt engineering, RAG, MLOps, and everything you need to be ready to be an AI-enabled data engineer!