How to Prepare for Data Engineer Interviews in 2026

[ Updated for 2026] :
[This article was originally written in 2022 and has been revised to reflect current Data Engineering and AI interview trends.]

In recent years, the exponential growth of data—driven by cloud adoption, digital products, IoT, and AI systems has made data a core business asset for almost every organization. As a result, companies across industries are heavily investing in data platforms to enable analytics, real-time insights, and AI-driven decision-making. This shift has significantly increased demand for Data Engineering roles, and Data Engineers continue to be among the most in-demand and strategically critical profiles in the IT industry.

By 2026, the role of a Data Engineer has evolved beyond traditional ETL development. Organizations now expect Data Engineers to design scalable, reliable, and AI-ready data platforms that can support analytics, machine learning, and Generative AI use cases. Modern data teams rely on Data Engineers to build robust ingestion pipelines, manage large-scale distributed systems, and ensure high-quality, well-modeled data that downstream consumers, including AI models can trust.

Because of this evolution, companies look for Data Engineers who are strong in:

• Programming (Python, SQL, and increasingly platform-specific SDKs)

• Advanced SQL and data transformations

• Distributed data processing and scalable pipeline design

• Data modeling for analytics and AI consumption

• Cloud-native architectures and cost-efficient design

• Data quality, observability, and governance

So in this blog post, I am going to cover all the topics and domains one can expect in Data Engineer Interviews

A. Programming Round

Most product-based companies, especially Meta, Apple, Amazon, Netflix, and Google (MAANG), place a strong emphasis on problem-solving skills and coding proficiency when hiring Data Engineers. These companies expect candidates to write clean, efficient, and optimized code, with a clear understanding of time and space complexity.

As a result, the first round of interviews in most MAANG and similar product companies is typically a coding round. This round may be conducted as:

• An online coding assessment, or

• A live coding / whiteboard interview, where candidates are asked to explain their approach while writing code.

The difficulty level of coding questions usually ranges from Easy to Medium, but interviewers focus heavily on:

• Logical thinking and edge-case handling

• Code readability and structure

• Optimization and complexity analysis

• Ability to explain the solution clearly

For Data Engineering roles, the problems often revolve around arrays, strings, hashing, basic data structures, SQL-like logic, and simple algorithmic patterns, rather than highly complex competitive programming problems.

To prepare effectively for these coding rounds, candidates can practice on the following popular and industry-relevant platforms:

• LeetCode – Most commonly used for MAANG-style interviews; excellent for data structures, algorithms, and SQL practice

• HackerRank – Widely used by companies for online screening tests, especially for SQL and problem-solving

• CodeSignal – Frequently used for structured coding assessments in product companies

• Codeforces – Useful for strengthening problem-solving skills and logical thinking

• GeeksforGeeks – Helpful for concept revision and interview-oriented explanations

While competitive programming expertise is not mandatory for Data Engineers, consistent practice on these platforms helps build confidence, speed, and clarity, which are critical for clearing the initial coding rounds at top product-based companies.

B. Technical Round

In many companies, the interview process begins with an initial technical screening round designed to assess whether a candidate has a solid grasp of the fundamental concepts required for a Data Engineering role. The objective of this round is not depth, but breadth and clarity of understanding.

This round typically includes questions related to:

• Basic programming and problem-solving

• Data structures and algorithms

• SQL fundamentals and query logic

• Distributed systems concepts

• End-to-end data pipelines and data flow

The questions are often conceptual or lightly hands-on, and interviewers primarily evaluate how clearly you think and communicate. It is not mandatory to answer every question perfectly, but you are expected to answer most questions correctly and confidently. Due to time constraints, candidates are encouraged to provide concise, structured answers, rather than deep dives into implementation details.

In recent years, this initial technical round has also started incorporating AI-adjacent data engineering topics, reflecting how modern data platforms support machine learning and Generative AI use cases. Candidates may be asked high-level questions around:

• Feature stores

• Vector databases

• Retrieval pipelines (RAG architectures)

• Real-time data feeds used by intelligent or agentic AI systems

Importantly, interviewers do not expect deep ML expertise at this stage. Instead, they look for an understanding of how data engineering enables AI systems, such as how data is ingested, transformed, stored, and served reliably for downstream models and agents.

To prepare effectively for this round, the following resources are particularly useful:

• Apache Spark documentation – For understanding distributed data processing concepts

• Apache Kafka documentation – Helpful for real-time data and streaming fundamentals.

C. System Design Round

Beyond basic concept-based questions, interviewers also evaluate how deeply you understand Data Engineering in practice. This part of the interview focuses on your ability to design and reason about real-world data systems, not just definitions.

Candidates are commonly asked questions around:

• Data pipelines and ETL/ELT workflows

• Batch and streaming data processing

• Scalable data architectures

• Failure handling and reliability

Interviewers expect you to explain how you would design, build, and maintain reliable, fault-tolerant data pipelines that can handle large volumes of data while meeting requirements around latency, cost, and data quality.

As part of this discussion, questions often involve popular data processing frameworks, such as:

• Apache Hadoop – For understanding distributed storage and large-scale batch processing
  

• Apache Spark – For batch and streaming data processing, performance optimization, and fault tolerance
  

• Apache Beam – For unified batch and streaming pipeline design across multiple runners
  

During these rounds, you should be able to clearly articulate:

• How data flows from source to destination

• How pipelines recover from failures

• How scalability is achieved as data volume grows

• Trade-offs between different tools and frameworks

Overall, this stage tests your Big Data fundamentals, system design thinking, and your ability to translate business requirements into robust, production-grade data pipelines. Check out more about that in Top Big Data Interview Questions

D. HR/Behavioural Round

Almost all companies conduct behavioral and HR interview rounds to evaluate whether a candidate is a good fit for the team and the organization, beyond just technical skills. The primary goal of this round is to assess how well you communicate, structure your thoughts, and articulate your ideas in a professional setting.

In this round, you can expect common HR questions such as:

• Why do you want to join this company?

• Why are you looking to change your current role?

• Why should we hire you over other candidates?

Interviewers also ask behavioral questions to understand how you work in real-world situations. Typical examples include:

• Tell me about a recent project you are particularly proud of

• Describe a situation where you had a conflict within your team and how you handled it

• Tell me about a time you faced a challenging deadline or failure

These questions are designed to evaluate your problem-solving approach, teamwork, ownership, and decision-making skills.

For this round, preparation is just as important as technical readiness. Candidates are strongly encouraged to prepare answers in advance, structure them clearly (for example, using the STAR method), and practice articulating them aloud before the interview. Well-prepared responses help you stay confident, concise, and impactful during the conversation.

Good Luck for the Interviews!!