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Our goal is not to simply deliver the project. We will help you build a hypergrowth environment around your technology and your mobile product.
Even the best code is just a part of success. We will provide you with interdisciplinary team of tech business talents, from project managers and strategy consultants to sales and marketing experts.
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Investing in data means investing in the future of your company that is based on actual research, not simple assumptions. With professional data analysts and data engineers on board, you’ll get answers to all the questions you might have about what should be the next step on your growth path.
Wherever there is a user, there is data as well. Every time someone visits your website or uses your app, data on what they have done is generated. This can be turned into an incredibly useful source of information on what your customers really want and how you can give it to them.
Why do random users leave your website instead of buying your product? What do your competitors do to reach new target audiences? With a good set of data, you can learn what can be done to expand your customer group and start generating revenue from new sources.
Is your marketing team really doing a great job as they are saying they are doing? Actual data will help you answer this question and tell you in which marketing options you should invest and which of them you should leave behind.
According to a report by McKinsey & Company, data-driven companies (the ones who make extensive use of customer analytics) are:
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.
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.
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:
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:
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:
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.
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.
Execute your vision with our trusted and battle-tested data engineers perfectly suited to your business needs.