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Hiring Data Engineers - a definitive guide

Data engineering is a critical component of any data-driven organization as it enables the collection, storage, and analysis of large amounts of data. The field of data engineering is broad and touches on many different industries, including finance, healthcare, and retail.

With the explosion of big data, the role of data engineers is becoming increasingly important as they are responsible for ensuring that data is cleaned, organized, and ready for analysis.

Data engineers are responsible for designing and building systems that can handle large amounts of data and make it accessible to data scientists and analysts. This includes tasks such as data ingestion, data warehousing, data integration, and data quality assurance. They also play a crucial role in developing and maintaining data pipelines, which are responsible for moving data from various sources to a centralized location for further analysis.

Credit: udacity

In addition to making the lives of data scientists easier, data engineers have the opportunity to make a tangible impact on the world by helping organizations make sense of the vast amounts of data that are generated every day.

We’ll be producing 463 exabytes per day by 2025. That’s one and 18 zeros of bytes worth of data every single day!

Fields like machine learning and deep learning can’t succeed without data engineers to process that vast amount of data. With the increasing adoption of machine learning and deep learning, the demand for data engineers is expected to continue growing as they are essential in the process.

The goal of this blog post is to provide a comprehensive overview of data engineering that will allow you to recruit the best data engineers for your organization. Let's start by understanding what data engineers do exactly!

What do Data Engineers do?


Here are some of the tasks that data engineers do:

  • Work on Data Architecture - They use a systematic approach to plan, create, and maintain data architectures while also keeping it aligned with business requirements. 
  • Collect Data - Before initiating any work on the database, they have to obtain data from the right sources. After formulating a set of dataset processes, data engineers store optimized data. 
  • Improve Skills & Tools - Data engineers don’t rely on theoretical database concepts alone. They must have the knowledge and prowess to work in any development environment regardless of their programming language. Similarly, they must keep themselves up-to-date with machine learning and its algorithms like the random forest, decision tree, k-means, and others. They are proficient in analytics tools like Tableau, Knime, and Apache Spark. They use these tools to generate valuable business insights for all types of industries. For instance, data engineers can make a difference in the health industry and identify patterns in patient behavior to improve diagnosis and treatment. Similarly, law enforcement engineers can observe changes in crime rates.
  • Create Models and Identify Patterns - Data engineers use a descriptive data model for data aggregation to extract historical insights. They also make predictive models where they apply forecasting techniques to learn about the future with actionable insights. Likewise, they utilize a prescriptive model, allowing users to take advantage of recommendations for different outcomes. A considerable chunk of a data engineer’s time is spent on identifying hidden patterns from stored data. 
  • Automate Tasks - Data engineers dive into data and pinpoint tasks where manual participation can be eliminated with automation.

The role of data engineers is fast changing, especially because the tools and technologies used by them have evolved exponentially over the last few years. But, this doesn’t mean that the role is getting simplified. It only means that new skills are now required to excel in the field. In fact, there are some early signs of new disciplines such as Analytics engineering that are being created within data engineering.

What is the difference Between Data Scientists, Data Engineers, and Data Analysts?


It is easy to get data engineers confused with data scientists and data analysts. Here is a quick summary:

What does a data engineer do?

Data Engineers procure data from numerous resources and convert the data by building and managing systems. They transform and clean the procured data for data scientists and analysts to scrutinize. They make the data viable by writing complex queries. Their role is very similar to that of software engineers and they need to have detailed knowledge of algorithms and some of the important concepts in programming.

What does a data scientist do?

After the data has been processed by the data engineer, the Data Scientist improves or optimizes it and shares it with the organization. Businesses need data scientists because they are the ones who come with in-depth knowledge of programming skills and statistical tools that can be used to provide analysis and insights that can aid in providing a fix to various business problems. They have the ability to transform data into actionable and beneficial insights for the organization.

What does a data analyst do?

A Data Analyst extricates data from the accumulated pool and uses various methods like data cleaning, conversion and modeling to comprehend this data. Their analysis and findings allows organizations to scrutinize various aspects of the business like its overall performance, market trends and needs and requirements of its clients that can be affected. Much like data scientists, the analysis provided by data analysts can help organizations make decisions that are heavily data-driven.

It may seem as though data scientists and data analysts are one and the same. The two roles certainly involve analyzing data. However, what sets one apart from the other is that most often data scientists create analyses and predictions for the future using data, while data analysts make use of the data to comprehend and make observations of the past.

What is the difference between a data engineer and a backend engineer?

A data engineer and a backend engineer are both important roles in the field of software development, but they have distinct responsibilities and focus on different aspects of the development process.

A data engineer is primarily responsible for designing, building, and maintaining the infrastructure and systems that support the collection, storage, and processing of large amounts of data. This includes tasks such as setting up and configuring data storage systems, developing data pipelines to move and transform data, and creating and implementing data models to support business needs.

On the other hand, a backend engineer focuses on the server-side of web development, building and maintaining the code that powers the application or website. This includes tasks such as designing and implementing APIs, connecting the application to a database, and ensuring the application's performance and scalability.

Both roles require a strong understanding of programming and data management, but a data engineer will have more expertise in data storage and processing technologies, while a backend engineer will have more experience with server-side languages and frameworks.

In a nutshell, a data engineer is responsible for the data infrastructure, and backend engineer is responsible for the server-side of the web application.

In a company, these roles often work closely together to ensure that the data infrastructure supports the needs of the application and that the application can access and utilize the necessary data.

What are some of the Job titles for Data Engineers?

Some of the job titles for Data Engineers in a company are:

  1. Data Architect -  The role of a data architect defines how the data will be stored, consumed, integrated as well as managed by various data entities and IT systems. They basically build complex computer database systems for the enterprises and work with the team who look after the needs of the database, which are available and needs to be maintained, etc.
  2. ML Engineer - The primary role of ML engineers is to design and implement machine learning algorithms as well as work with large volumes of structured and unstructured data. You need to be able to design and develop high quality, production-ready code which can be used by the users of cloud platforms in an organization. They are responsible for delivering the models in the environment with the focus of using machine learning techniques in order to monitor, manage and improve the data quality. More info about recruiting ML engineers on our blog post about the topic here!
  3. Data Warehouse Engineer -  The role of a data warehouse engineer is to look after the full back-end development of the data warehouse and is also responsible for ETL processes, performance administration, dimensional design, etc, of the table structure. They work closely with the data scientists, data engineering teams, data analysts, etc.
  4. Technical Architect - They are responsible for breaking down the large projects into manageable pieces, you will define the overall structure of a system and aim at improving the business of an organization. You will determine which IT product should be used in order to analyze the cost-benefit in an organization.
  5. Solutions Architect - The role of a solution architect is to lead the practice and introduce the overall technical vision for a specific solution. They are responsible for finding the best solution to solve the business problems, providing specifications for defining the solutions as well as managed and delivered, ensuring a good quality solution, implemented as per the architecture defined, supporting the build and the operation, and conducting quality and architecture reviews at key checkpoints in the Solution Architecture Development Lifecycle, etc.

What skills and technologies do Data engineers know?


Some of the most important skills and knowledge areas that are essential for data engineers are:

  • Programming languages: Knowledge of various coding languages like Javascript, Python, and Scala is important for data engineers as they may need to write code to perform data manipulation and analysis tasks.
  • SQL Mastery: SQL is a crucial language for data engineers as it allows them to communicate with databases and retrieve data. They should have a good understanding of SQL and be able to use advanced techniques like correlated subqueries and window functions.
  • Architectural Projections: Data engineers should have a good understanding of various libraries, tools, resources, platforms, and technologies that are used in data engineering. This includes database management systems, computation, stream processors, workflow orchestrators, message queues, and serialization formats.
  • Data Modeling Techniques: Data engineers should have a thorough understanding of data modeling techniques, including normalization and denormalization trade-offs, entity-relationship modeling, and dimensional modeling.
  • ETL (Extract, Transform and Load): Data engineers should be familiar with the ETL process, which is used to create a single data source by amalgamating data from numerous data sources. They should also know how to write ETL scripts that can adapt to evolution.
  • Data Storage: Data engineers should have a good understanding of data storage technologies, including data warehouses and data lakes, and know how to choose the right storage solution for a particular use case, e.g. NoSQL, PostgreSQL, GraphDB
  • Cloud Computing: As more and more organizations are moving to the cloud, knowledge of cloud computing and storage is becoming increasingly important for data engineers, e.g. AWS, Azure, Google Cloud
  • Big Data Tools: Data engineers may work with big data from time to time, so it's important for them to be familiar with popular big data tools and technologies like Kafka, Hadoop, and MongoDB.

What is a good boolean to use for searching Data engineers?

A good Boolean search for data engineers would include specific keywords and operators to filter the results and find candidates with the desired skills and experience.

Here's an example of a Boolean search string that you could use to search for data engineers with experience in Python and ETL:

("data engineer" OR "data architect") AND ("Python" OR "Scala" OR "Java") AND ("ETL" OR "data pipeline" OR "data integration")

This search string uses the keywords "data engineer" and "data architect" to search for candidates with experience in data engineering. The keywords "Python", "Scala", and "Java" are used to search for candidates with experience in specific programming languages. The keywords "ETL", "data pipeline" and "data integration" are used to search for candidates with experience in data integration and data pipeline.

You could also use other keywords and phrases that are specific to your needs, such as "big data", "cloud computing" or "machine learning".

It's worth noting that different sources, such as job portals, LinkedIn or Github, may use different syntax and operators, so you may need to adjust the search query accordingly.

In summary, a good Boolean search for data engineers should include specific keywords and operators that are relevant to the desired skills and experience, and be adjusted accordingly to the source where you are searching.

What are Sample Interview questions for Data Engineers?

Here are some sample questions - both for you to ask candidates as well as to help them prepare.

Hard Skills

Logic & Algorithms
  1. What are the differences between structured and unstructured data?
  2. What are big data’s four Vs
  3. Explain indexing.
  4. Explain the Snowflake Schema in Brief
  5. What are Skewed tables in Hive?
  6. Explain how columnar storage increases query speed.
  7. What is orchestration?
  8. What was the algorithm you used in a recent project?
  1. What is Normalisation and Denormalisation? When do we use them? Give a real-time example that is implemented in your project. 
  2. What are the design schemas of data modeling?
  3. What is the difference between a data warehouse and an operational database?
  4. Point out the difference between OLTP and OLAP.
  5. While deploying a Big Data solution, what steps must you follow?
Programming Languages & Tools
  1. What are *args and **kwargs used for?
  2. There are 10 million records in the table and the schema does not contain the ModifiedDate column. One cell was modified the next day in the table. How will you fetch that particular information that needs to be loaded into the warehouse? 
  3. Difference between Parquet and ORC file. Why does the industry use parquet over ORC? Can schema evolution happen in ORC
  4. Anagram coding question without using the sorted function or counter function
  5. Tell me some of the important features of Hadoop
  6. Which ETL tools have you worked with? What is your favorite, and why?
  7. What is the Heartbeat in Hadoop?
  8. Which Python libraries would you recommend for effective data processing?

Behavioral / Soft Skills

  1. What scale of data have you worked with in the past?
  2. What experience do you have acting as a liaison and working with the departments that use your data?
  3. Tell me about a time you had an ETL performance issue. How did you troubleshoot and come up with a solution?
  4. Describe the most challenging project you’ve worked on. What was your role?
  5. Describe a time when you found a new use case for an existing database.

How much do data engineers earn?

Data engineering is a well-paying career. The average salary in the US is $115,176, with some data engineers earning as much as $168,000 per year, according to Glassdoor (May 2022).

In general, our experience at Rocket indicates that data engineers make on par with backend engineers and potentially slightly more if they have a deeper machine learning background as well.


To summarize, A data engineer is primarily responsible for designing, building, and maintaining the infrastructure and systems that support the collection, storage, and processing of large amounts of data.

Data engineers are critical to the modern organization given the volume of data that is generated on a constant basis. The key to recruiting fantastic data engineers is to understand the job description in depth and utilizing your knowledge of data engineering concepts to interview and vet candidates effectively. Good luck!

About Rocket

Rocket pairs talented recruiters with advanced AI to help companies hit their hiring goals and knows technology recruiting inside out. Rocket is headquartered in the heart of Silicon Valley but has recruiters all over the US & Canada serving the needs of our growing client base across engineering, product management, data science and more through a variety of offerings and solutions.

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