According to a report by McKinsey & Company, data-driven companies (the ones who make extensive use of customer analytics) are:
- 23 times more likely to beat their competitors in terms of acquiring new customers
- 9 times more likely to beat their competitors in customer loyalty
- 19 times more likely to achieve above-average profitability
Sounds like something you want to see happening at your company? If yes, then it’s time to finally invest in data analytics — and the whole process starts with hiring a data engineer.
What does a data engineer do?
It’s best to answer this question by looking at the difference between the role of the data analyst and the data engineer itself.
The job of a data analyst is to thoroughly review the data available. Based on it, reports are prepared, used mostly to make sure that the decisions made at a company are driven by research and not simple, unconfirmed assumptions. Data analysts can start their analysis with a hypothesis prepared beforehand, or figure out one during the exploratory research.
Where does the data being reviewed come from, though? Does it magically appear on the analyst’s computer? Obviously, not. Here, actually, is where data engineers come into play. Their role is to pull out the data from as many relevant sources as possible and later deliver it to data analysts for review.
Responsibilities of a data engineer
Most of the data engineer role focuses on building and maintaining a steady data pipeline that regularly delivers new content to data analysts. To accomplish this, highly sophisticated algorithms are written. The goal behind these algorithms is to retrieve the data in a required form from user logs, customer support tickets, heatmaps, and even external sources. Everything that can help understand how the product works and what more does it need is useful — nothing can be left aside.
This process of extracting data from various places is often referred to as data mining. After it happens, the raw data must be translated (transferred) into a form easily understandable by data analysts. This can include, for example, writing algorithms that will place specific types of data into different columns in a sheet.
After the data is transferred, it’s usually kept in a database called data warehouse. Establishing and maintaining one is another important part of a data engineer role. Thanks to data warehouse, it’s later easier for data analysts to retrieve historical data and research trends based on it.
All this establishes the most typical ETL pipeline architecture used in data engineering. ETL stands for Extract, Transform, Load.
Data engineers might be also responsible for:
- Developing data analytics dashboards to be used by various teams within the company.
- Preparing data reports for stakeholders.
- Managing data lakes (unlike it’s the case with data warehouses, the data stored in lake is raw, unprocessed, more difficult to be accessed and understood by a non-engineer).
- Detecting suspicious anomalies in data.
The most important technical data engineer skills
Another thing that makes data analysts and data engineers differ is usually their background. While the former are usually some kind of statisticians working with Python, sheets, and probability, the latter are often computer science graduates with knowledge more similar to the knowledge possessed by a typical developer.
Some of the most typical technical data engineer skills required for the job are:
- Expert knowledge in database management technologies (SQL, NoSQL).
- Proficiency in programming languages used for data science (Python, Scala, R, Java, C++, other), as well as the popular libraries and frameworks dedicated to these technologies.
- Good understanding of the ETL model and previous experience in building such pipelines.
- Understanding of machine learning concepts.
- Experience in data warehousing and tools used for it (e.g. Amazon Redshift, Panoply).
- Experience in cloud data management (e.g. with Amazon Web Services).
- Experience with Apache Hadoop or other similar tools. Hadoop is used for creating links between multiple computers so they can help in solving issues requiring use of huge amounts of data.
- Experience with data analysis.
The non-technical set of data engineer skills
- Team player
Because one of the most important goals of a data engineer is to deliver content to data analysts, being a great team player is exceptionally important in this role. Regular catch-ups between DE and DA should happen to align on the current business needs and to figure out together the best solutions to meet these requirements.
- Experience in working with non-devs and non-data people
Working as a data engineer requires being constantly in touch with non-technical teams at the company. Because of this, good data engineer should also be able to translate their own jargon into language easily understandable by someone who doesn’t have that much experience with data.
- Business-oriented approach to work
While data analysts usually provide specific guidelines on what data they exactly require, data engineers should also be able to think about the current business needs and figure out the best ways to provide content that might be useful for the company.
- Creativity
Although there are a lot of best practices to consider when working as a data engineer, there is also space for creativity: finding new sources of data, ways to catalogue it fitting the company, optimising the pipeline with limited resources, etc. There are always multiple solutions to approach an issue and it’s up to the data engineer which to use.
- Expert multitasking
Data engineers are often involved in a few different tasks every day, making it important for them to manage time well. They also need to consider that always a sudden issue might happen that will interrupt their preplanned schedule.
Best data engineer interview questions
Running a technical interview when hiring a data engineer is crucial — it will allow you to assess whether you are hiring a truly experienced professional. If you don’t feel confident enough in the field of data management, you might consider asking an expert to help you out with the interview process. This could be either another data engineer or a knowledgeable IT Project Manager.
To get you ready for what’s coming, here are some of our favorite data engineer interview questions:
- What are the core methods of Hadoop Reducer?
- What are Star Schema and Snowflake Schema?
- What would be your approach to transferring a 1.000.000+ gigabytes log files into a database?
- What are the differences between relational and non-relational databases?
- What are the differences between SQL and NoSQL? In what situations one is preferred over the other?
- Which ETL tools are you the most comfortable with? Why did you choose these specific tools instead of the others?
- What is a linked list?
Besides an interview, additional tests of knowledge can be run in order to assess the true experience of your potential new data engineer. These usually are quick tasks to be done at home (within 1-7 days from receiving it) or whiteboard exams done during the technical interview. Before you run either of these, remember to consult the best approach to tests with another data engineer or IT Project Manager experienced in the field of data management — they will be able to look into your current process and help you find out what exactly you might want to check during the technical interview.
Making the most out of data
Data scientists are a great asset when it comes to understanding what your customers actually think and want, but you also need people in your team who will be able to turn these needs into a functioning product.
If you are looking for great developers (including full-stack devs), designers, AI experts, projects manager, or product owners, you came to the right place. At Ideamotive, we developed, and constantly expand, our network of top IT talents looking for new work opportunities and challenges. Contact us today to connect with them and build a future award-winning team.
