Data Engineering 101: What is Data Engineering?
Career · 12 Jan 2023, 08:26 · 4 mins read ·

If you're new to the world of data, you might have heard the term Data Engineer. But do you know the difference between an Analyst, a Data Scientist, and a Data Engineer is? Do you know what a Data Engineer does with their time? In this post, Zindi Ambassador and analyst engineer Odeajo Israel will explain data engineering, explore the data engineering life cycle, and talk about how a Data Engineer adds value to an organisation.

So, first things first:

What is data engineering?

From a layman’s understanding of the definition of data engineering, it sounds like the engineer in charge of data.

Data engineering is a science that pays more attention to designing, collecting, processing, analysing, and building data. Most times, the data in question is LARGE and SCALABLE. The process of engineering the data helps to maintain and scale both structured and unstructured data, allowing other parts of the business to use that data to make decisions and deliver products.

The data engineering life cycle

  1. Data collection
  2. Data sourcing
  3. Data storing
  4. Data analysis
  5. Data modelling

Most of the time, data engineering involves all the possible actions you can perform on your data from start to end. The processes aim to make the best sense of all available data.

A data engineering case study

Taken from, here is sample case study question for a Data Eengineer:

“You’re tasked with building a data pipeline for POS data from a store like Walmart. This data will be used by data scientists. How would you do it?”

With a case study question, the first step is to ask clarifying questions. You should gather as much information as you need. Then, propose your solution.

A few tips for tackling a data engineering case study:

  • Problem-solving approach: When you’re presented with a problem, interviewers want to know the steps you will take to solve it.
  • Thoroughness: Before you jump into an answer, get clarification. You should understand exactly what they’re looking for. You'll be better placed to give a good answer.
  • Ability to communicate: Think out loud and walk the interviewer through the process. Say exactly why you would make a particular choice.
  • Design patterns: With architecture problems, you should have a strong grasp of design patterns, as well as the technologies and products that can be used to solve the problem.
  • Forward thinking: Every data engineering solution includes trade-offs. Interviewers want to see that you can assess a solution in terms of pros and cons, as well as potential weaknesses of a solution.

Ultimately, these questions focus on a range of subjects including database design, data warehousing, ETL pipelines, and data modelling.

Relevant value added by a Data Engineer

People or teams working with data - including data analysts, data engineers, and data scientists - are always looking for effective ways of preparing and transforming data, generating efficient data models at any scale, and creating a self-service experience for themselves and their counterparts on the business side. Nonetheless, they are frequently tested with getting a handle on disorganised or missing data systems, and are expected to build systems to handle a wide array of requests and queries from all parts of the business.

Who is fit for a data engineering role?

A Data Engineer must have a programming background. Technical skills needed include a proficiency in SQL, Python, R, and ETL approaches and practices. Additionally, you should have an interest in the heirarchy and structure of information, and a willingness to grapple with tough problems. Building and managing such a complex frameworks and information pipelines requires someone with determination and creativity as well as critical thinking, technical skills, and the ability to think and work independently.

About the author

Odeajo Israel is a Google TensorFlow Certified professional with four years of experience in the analysis sector. He helps organisations make data-driven decisions and design metrics specific to their organisation. Israel is also a Zindi ambassador for Nigeria. He is enthusiastic about topics such as deep learning, machine learning, big data, and artificial intelligence. In Nigeria, he is one of the co-organisers and facilitators of the AI movementt. He leads meetups, workshops, and events with the goal of constructing a community of data scientists who can tackle local problems. You can reach him on LinkedIn.

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