Hire Veteran Data Engineers

Step into the hypergrowth path with the help of our experienced data engineers perfectly fitted for your needs.

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We deploy fully operational teams of talents perfectly fitted for your industry, market and your company culture.

With our expert network your product is in the best hands available.

What makes us different?

Wide Range Of Experts

Our network of thousands of talents combines on-site talents, off-site collaborators and top software houses. We will pick data engineering superstars perfectly fitted for your company culture, industry and technology.

Hypergrowth Approach

Our goal is not to simply deliver the project. We will help you build a hypergrowth environment around your technology and your mobile product.

Hollistic Business Support

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.

Truly Agile Process

We take the best principles of agile approach to software develolpment and expand it to other project's areas, to ensure the highest efficiency and transparency of our expert's work.

How does it work in practice?

You tell us about your challenges and business goals.

Based on that information we provide you with top software talents perfectly matching your needs.

Together we brief the newly created team and kick-off the project

Poof! Your tailor-made squad of experts is ready to work!

WE ARE PROUND OF OUR DATA ENGINEERING PROJECTS

Mediatask: building a marketplace for hundreds of draftsmen and architects

How we re-written the product completely with intelligent algorithms and introduced various business automation solutions allowing to scale almost infinitely.

Read the success story
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Read the success story

Ideamotive's team is extremely talented at developing strategy and is flexible when it comes to scaling according to the business need.

View the case study

Jacek Jaskólski

CEO, Mediatask

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Hyper grow your business with us

Our network of software talents is waiting for the next challenge.

Leverage the full power of your data and step into the hypergrowth path

Make smarter business decisions

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.

Understand your existing customers

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.

Attract new customers

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.

Make your marketing data-driven

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.

How To Find The Best Data Engineer Suitable For Your Project?

According to a report by McKinsey & Company, data-driven companies (the ones who make extensive use of customer analytics) are:

  1. 23 times more likely to beat their competitors in terms of acquiring new customers
  2. 9 times more likely to beat their competitors in customer loyalty
  3. 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:

  1. Developing data analytics dashboards to be used by various teams within the company.
  2. Preparing data reports for stakeholders.
  3. 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).
  4. 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:

  1. Expert knowledge in database management technologies (SQL, NoSQL).
  2. 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.
  3. Good understanding of the ETL model and previous experience in building such pipelines.
  4. Understanding of machine learning concepts.
  5. Experience in data warehousing and tools used for it (e.g. Amazon Redshift, Panoply).
  6. Experience in cloud data management (e.g. with Amazon Web Services).
  7. 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.
  8. Experience with data analysis.

The non-technical set of data engineer skills

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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:

  1. What are the core methods of Hadoop Reducer?
  2. What are Star Schema and Snowflake Schema?
  3. What would be your approach to transferring a 1.000.000+ gigabytes log files into a database?
  4. What are the differences between relational and non-relational databases?
  5. What are the differences between SQL and NoSQL? In what situations one is preferred over the other?
  6. Which ETL tools are you the most comfortable with? Why did you choose these specific tools instead of the others?
  7. 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.

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