The Ultimate Guide to Data Engineering Interviews in 2026
Data engineering is one of the fastest-growing and most in-demand fields in the technology industry today. According to a recent report from IBM, the number of data engineering jobs is projected to grow by 28% over the next five years, much faster than the average for all occupations [1].
As companies in every industry become increasingly data-driven, the need for skilled data engineers to build and maintain data infrastructure has never been greater. But what exactly does it take to succeed as a data engineer in 2023? And what can you expect in a data engineering interview?
In this comprehensive guide, we‘ll dive deep into the most essential concepts, skills, and technologies you need to know for data engineering interviews this year. Whether you‘re a seasoned data professional or just starting your career, this article will provide you with the knowledge and insights you need to ace your next interview and land your dream data engineering job.
The Rise of Data Engineering
Before we jump into the specifics of data engineering interviews, let‘s take a step back and look at why data engineering has become so crucial in today‘s business landscape.
Over the past decade, we‘ve seen an explosion in the amount of data being generated and collected by organizations of all sizes. According to a report from IDC, the global datasphere (i.e., the total amount of data created, captured, and replicated) is expected to grow from 64.2 zettabytes in 2020 to 175 zettabytes by 2025 [2].
This exponential growth in data has been driven by a number of factors, including:
- The proliferation of mobile devices and IoT sensors
- The rise of social media and user-generated content
- The digitization of business processes and transactions
- The increasing adoption of cloud computing and big data technologies
All of this data presents enormous opportunities for businesses to gain insights, improve operations, and drive innovation. However, it also presents significant challenges in terms of how to effectively capture, store, process, and analyze such vast amounts of information.
This is where data engineers come in. Data engineers are responsible for designing, building, and maintaining the infrastructure and pipelines that enable data to flow seamlessly from source systems to data consumers. They work closely with data scientists, analysts, and business stakeholders to ensure that data is reliable, scalable, and can support the organization‘s analytics and decision-making needs.
According to a survey by Forrester, 97% of data and analytics decision-makers say that data engineering is crucial to their firms‘ ability to use data and AI to drive business outcomes [3]. And as the volume and complexity of data continues to grow, the demand for skilled data engineers is only going to increase.
Data Engineering vs. Data Science
One common point of confusion is the difference between data engineering and data science. While these two fields are closely related and often work together, they focus on different aspects of the data lifecycle.
Data scientists are primarily focused on extracting insights and building machine learning models from data. They use statistical and computational techniques to analyze datasets, test hypotheses, and develop predictive models that can drive business decisions.
Data engineers, on the other hand, are focused on the infrastructure and processes that make data science possible. They build the pipelines that collect, store, and transform data from various sources, ensuring that it is cleaned, formatted, and structured in a way that makes it usable for analysis. They also develop the tools and platforms that enable data scientists to access and work with data at scale.
To put it simply, data engineers build the foundation that enables data scientists to do their work. Without robust data engineering practices, data science initiatives are likely to fail or deliver subpar results.
According to a survey by Anaconda, 65% of data scientists say they spend the majority of their time on data preparation and cleansing, rather than actual analysis or modeling [4]. This highlights the critical importance of data engineering in the analytics workflow.
Core Data Engineering Skills
So what skills do you need to succeed as a data engineer in 2023? While the specific tools and technologies may vary depending on the organization and use case, there are several core competencies that every data engineer should possess:
1. SQL and Database Design
At the heart of any data engineering role is the ability to work with structured data stored in relational databases. SQL (Structured Query Language) is the standard language for querying and manipulating relational data, and it‘s an absolute must-have skill for data engineers.
In addition to being proficient in SQL syntax and commands, data engineers need to understand database design principles and how to structure data in a way that is efficient, scalable, and supports the intended use cases. This includes concepts like normalization, indexing, partitioning, and query optimization.
Some common SQL databases that data engineers should be familiar with include:
- MySQL
- PostgreSQL
- Oracle
- Microsoft SQL Server
2. Big Data Technologies
While SQL databases are great for structured data, they often struggle to handle the volume, variety, and velocity of big data workloads. That‘s where big data technologies come in.
Hadoop is one of the most well-known big data platforms, providing a framework for distributed storage and processing of large datasets across clusters of commodity servers. Key components of the Hadoop ecosystem include:
- HDFS (Hadoop Distributed File System): A distributed file system that provides high-throughput access to application data.
- MapReduce: A programming model for processing and generating large datasets in parallel.
- YARN (Yet Another Resource Negotiator): A cluster management technology that allows multiple data processing engines to run on the same shared cluster.
- Hive: A data warehousing application that provides SQL-like querying of large datasets stored in HDFS.
- Pig: A platform for analyzing large datasets using a high-level scripting language.
In recent years, Spark has emerged as a faster and more flexible alternative to Hadoop‘s MapReduce engine. Spark provides in-memory processing and a unified framework for batch processing, real-time streaming, machine learning, and graph processing.
Other big data technologies that data engineers should be familiar with include:
- Kafka: A distributed streaming platform for building real-time data pipelines and streaming applications.
- Cassandra: A highly scalable NoSQL database for handling large amounts of structured data across commodity servers.
- HBase: A column-oriented NoSQL database that runs on top of HDFS.
3. Cloud Computing
Cloud computing has revolutionized the way organizations store, process, and analyze data. By providing on-demand access to virtually unlimited storage and computing resources, cloud platforms like AWS, GCP, and Azure have made it easier than ever to build and scale data pipelines.
Data engineers need to be proficient in working with cloud services and architectures, including:
- Storage services like Amazon S3, Google Cloud Storage, and Azure Blob Storage for storing unstructured data.
- Compute services like AWS EC2, Google Compute Engine, and Azure Virtual Machines for running data processing workloads.
- Managed data services like Amazon Redshift, Google BigQuery, and Azure Synapse Analytics for data warehousing and analytics.
- Serverless computing services like AWS Lambda, Google Cloud Functions, and Azure Functions for building event-driven data pipelines.
In addition to the core services, each cloud provider offers a wide range of specialized data and analytics services, such as AWS Glue for ETL, Google Cloud Dataflow for stream and batch processing, and Azure Databricks for Spark-based analytics.
4. Data Pipelines and ETL
Data pipelines are the backbone of any data-driven organization, enabling the flow of data from source systems to analytics and machine learning platforms. Data engineers are responsible for designing, building, and maintaining these pipelines, ensuring that data is efficiently extracted, transformed, and loaded (ETL) into the appropriate systems.
Key concepts and technologies for data pipelines include:
- Batch vs. streaming architectures
- Data partitioning and parallelization
- Data quality and validation
- Workflow orchestration tools like Apache Airflow and Luigi
- ETL tools like Talend, Informatica, and AWS Glue
- Stream processing frameworks like Apache Beam and Flink
5. Data Modeling and Architecture
Data modeling is the process of creating a logical representation of an organization‘s data assets, including entities, attributes, and relationships. Effective data modeling is critical for ensuring that data is organized and structured in a way that supports the intended use cases and enables efficient querying and analysis.
Data engineers need to be familiar with various data modeling techniques, including:
- Relational modeling (e.g., star and snowflake schemas)
- Dimensional modeling
- NoSQL data modeling (e.g., document, key-value, and graph models)
- Data vault modeling
In addition to data modeling, data engineers need to understand how to design scalable and performant data architectures. This includes concepts like:
- Lambda and Kappa architectures for combining batch and streaming processing
- Microservices architectures for building modular and scalable data pipelines
- Data mesh architectures for enabling domain-driven data ownership and governance
- Data lake and data lakehouse architectures for storing and processing raw and curated data
6. Machine Learning and AI
In recent years, machine learning (ML) and artificial intelligence (AI) have become increasingly important in the data engineering landscape. As organizations seek to build more intelligent and automated systems, data engineers play a critical role in enabling the development and deployment of ML models.
Some key ML concepts and techniques that data engineers should be familiar with include:
- Feature engineering: The process of selecting and transforming raw data into features that can be used to train ML models. This includes techniques like one-hot encoding, feature scaling, and feature selection.
- Model deployment: The process of integrating trained ML models into production systems for real-time or batch inference. This includes considerations like model serialization, versioning, and monitoring.
- MLOps: The practice of applying DevOps principles to the ML lifecycle, including automating the building, testing, and deployment of ML models.
- Tools and frameworks: Popular ML tools and frameworks that data engineers should be familiar with include scikit-learn, TensorFlow, PyTorch, and Apache MXNet.
The Future of Data Engineering
As we look ahead to the future of data engineering, there are several key trends and technologies that are shaping the field:
AI and ML Automation
One of the biggest trends in data engineering is the increasing use of AI and ML to automate various aspects of the data lifecycle. This includes:
- Automated data discovery and profiling tools that use ML to identify patterns and anomalies in data.
- Automated ETL tools that use AI to map source data to target schemas and generate transformation code.
- Automated data quality tools that use ML to detect and correct data errors and inconsistencies.
- Automated feature engineering tools that use AI to identify relevant features and generate feature sets for ML models.
By automating these tasks, data engineers can spend less time on manual data wrangling and more time on higher-value activities like data architecture and pipeline design.
Serverless Computing
Serverless computing is an emerging paradigm that enables developers to build and run applications without having to manage underlying infrastructure. In a serverless architecture, the cloud provider dynamically allocates computing resources based on the needs of the application, and the developer is only charged for the actual amount of resources consumed.
Serverless is well-suited for data engineering workloads, particularly for event-driven and real-time processing. Some key benefits of serverless for data engineering include:
- Automatic scaling based on workload demands
- Reduced operational overhead and infrastructure management
- Pay-per-use pricing model
- Faster development and deployment cycles
Some popular serverless platforms for data engineering include AWS Lambda, Google Cloud Functions, and Azure Functions.
Data Mesh
Data mesh is an emerging architectural paradigm that seeks to address the challenges of centralized data management in large organizations. In a data mesh architecture, data ownership and governance are distributed across the organization, with each domain team responsible for their own data products.
The core principles of data mesh include:
- Domain-oriented data ownership and architecture
- Data as a product
- Self-serve data infrastructure as a platform
- Federated governance model
By adopting a data mesh approach, organizations can enable faster and more agile data product development, improve data quality and reliability, and foster a culture of data-driven decision making.
Conclusion
Data engineering is a complex and rapidly-evolving field that plays a critical role in enabling data-driven organizations. As the demand for data insights continues to grow, so too does the need for skilled data engineers who can design, build, and maintain scalable data infrastructures.
To succeed in a data engineering role in 2023, you‘ll need a combination of technical skills, domain knowledge, and business acumen. This includes proficiency in SQL and database design, big data technologies like Hadoop and Spark, cloud computing platforms, data pipelines and ETL, data modeling and architecture, and machine learning and AI.
But beyond the technical skills, succeeding as a data engineer also requires strong problem-solving abilities, communication skills, and a customer-centric mindset. The best data engineers are those who can not only build robust and scalable systems, but also understand the business context and use cases behind the data.
As you prepare for your next data engineering interview, focus on building a strong foundation in the core skills and concepts discussed in this guide. But also don‘t be afraid to showcase your unique experiences, projects, and perspectives. The most successful candidates are often those who can demonstrate not only technical proficiency, but also creativity, curiosity, and a passion for using data to drive business value.
Additional Resources
If you‘re looking to deepen your knowledge and skills in data engineering, here are some additional resources to check out:
- The Data Engineering Cookbook by Andreas Kretz
- Designing Data-Intensive Applications by Martin Kleppmann
- Data Pipelines Pocket Reference by James Densmore
- Fundamentals of Data Engineering by Joe Reis and Matt Housley
- The Ultimate Guide to Acing Your Data Science Interview
Best of luck on your data engineering journey!
