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Big Data Engineer - How to Recruit The Right One

Data is something that we all often encounter in our daily lives. It can be anything like music or video transferred from a mobile device to another mobile device, it can be data when using 4G internet, or all our photos on the drive or simply data when we log in to our social network account. And day after day, life continues with the huge amount of data exchanged and stored, which leads to realize Big Data Engineer as a career that needs to be developed and appreciated.

Along with time and progress in IT, the need to use social networks specifically for businesses has also increased. We can see platforms such as WhatsApp and Skype now focused on developing business versions. The reason is that we are gradually moving into the data age. If we analyze daily activities, we will realize that data is something that we continue to use now and forever.

The general concept of Big Data

In fact, Big Data is often defined as a large data set, including structured, unstructured or incompletely structured data, each of which can be used to exploited into detailed information. These data files are often so big and complex that normal data processing software can hardly collect, store and process it for a reasonable amount of time, because its size can be up to petabytes (millions of gigabytes) or even exabytes (equal to 1e + 9 gigabytes)

Generally, Big Data is characterized by three V, including:

  • Volume: Size of data;

  • Variety: the diversiform extent of the data;

  • Velocity: the speed that data needs to be processed and analyzed.

In short, Big data is made up of sources including websites, social media, desktops and mobile applications, scientific experiments, sensors and other internet of things (IoT).

Hence, Big data engineer is a vital member in any enterprise data analytics team. His roles are to manage, optimize, oversee and monitor data retrieval, storage and distribution throughout the company.

Roles and Responsibilities of a Big Data Engineer?

Most of us have an idea about who a big data engineer is. However, we are confused about his responsibilities and roles. Due to the appearance of “big data”, the area of responsibility has changed dramatically. These experts wrote large SQL queries and overtook data using tools like Informatica ETL, Pentaho ETL, Talend before. Nowadays, their scope of work has expanded.

Roles of Big Data Engineer

Firstly, we can typically find generalists in small teams or in small companies. With this setting, big data engineers wear many hats like one of the few ‘data-focused’ people in the company. These generalists are responsible for every step of the data process including managing data, analyzing, etc. This is a good role if we are looking to transition from data science to data engineering. Smaller businesses will not have to worry much about engineering ‘for scale’.

Secondly, pipeline-centric. People can see this role in midsize companies. Pipeline-centric data engineers work alongside data scientists to make use of the data they collect. These big data engineers need in-depth knowledge of distributed systems and computer science.

Lastly, database-centric. In larger companies, management of the flow of data is a full-time job. Therefore, role of a big data engineer is to focus on analytics databases. They work with data warehouses across multiple databases and are responsible for developing table schemas.

Responsibilities of Big Data Engineer

As a result, the big data engineer will manage, organize data. So, they keep an eye out for trends or inconsistencies that will impact business objectives as well. This is really a highly technical position. It requires experience and skills in areas such as programming, mathematics and computer science. However, big data engineers also need soft skills to communicate data trends to others in the company. This is to help the business make use of the data it collects. We make a list of the most common responsibilities of a big data engineer including:

  • Developing, constructing, testing and maintaining architectures

  • Aligning architecture to meet with business requirements

  • Collecting data

  • Developing processes of data set

  • Using programming languages and tools

  • Know about ways of improving data reliability, efficiency and quality

  • Implement research for questions of industry and business

  • Use large data sets to address business issues

  • Deploying sophisticated analytics programs

  • Preparing data for predictive and prescriptive models

  • Finding out hidden patterns using data

  • Using data to detect tasks that can be automated

  • Providing updates to stakeholders based on analytics

Requirements for Big Data Engineer position

The industry of big data has grown significantly during the last decade. Therefore, skills started to be more specific as well. Accordingly, there are many tools that have become very significant on their own. This job has required higher and more multiple qualification and skill.

Studying higher education degrees in computer science, engineering

In fact, to take on big data engineer position, we need a bachelor’s degree in computer science, software engineering, applied math, physics, statistics or a related field. In addition, we need to have many real-world skills to qualify for most entry-level positions. Moreover, we should consider a master’s degree in computer engineering or computer science. This is to fine tune our skills and expand our knowledge.

  • Technical Skills for Data Engineers

  • Statistical analysis and modeling

  • Database architectures

  • Hadoop-based technologies (like MapReduce, Hive and Pig)

  • SQL-based technologies (like PostgreSQL and MySQL)

  • NoSQL technologies (like Cassandra and MongoDB)

  • Data modeling tools (like ERWin, Enterprise Architect and Visio)

  • Python, C/C++ Java, Perl

  • MatLab, SAS, R

  • Data warehousing solutions

  • Predictive modeling, NLP and text analysis

  • Machine learning

  • Data mining

  • UNIX, Linux, Solaris and MS Windows

Some business skills for big data engineer

Firstly, we should have creative problem-solving skill. According, we can approach data organization challenges with a clear eye on what is crucial. The engineer uses the right approach/methods to make the maximum use of time and human resources.

Futhermore, a good big data engineer can collaborate effectively. So, we carefully listen to management, data scientists and data architects to determine their needs. It’s better to have intellectual curiosity. The engineer can explore new territories and look for creative and unusual ways to address data management problems.

The engineer has industry knowledge. We have to understand obviously the way our chosen industry functions. Besides, keep in mind how data can be collected, analyzed and utilized. Last but not least, we have to maintain flexibility in the face of big data developments.

Learn additional professional engineering/ big data certifications

If we want to buff up specific skills, we should find a lot of vendor-specific certifications (e.g. Oracle, Microsoft, IBM, Cloudera etc.). To know which ones are worth our investment, ask our mentors for advice, and examine recent job descriptions for ideas.

Certified Data Management Professional (CDMP)

It is a solid, all-round credential for database experts in general. Most employers will recognize the acronym on our CV.

Google Cloud Certified Professional Data Engineer

This certificate demonstrates our abilities in building and maintaining data structures, analyzing data, designing processing systems, and incorporation of security. It is about compliance and reliability in processes and structures as well.

Cloudera Certified Professional (CCP) Data Engineer

It shows that we can exhibit skills in data ingestions, transformation, staging and storing of data. Moreover, we has analysis of data from Parquet, Avro, JSON, and others and the development of linear and branching workflow among other abilities.

Recruit Big Data Engineer for our company effectively

High-volume, high-speed transactional data is really a game-changer for those companies that use it correctly. However, we need an experienced team of big data engineers to make the most of it. The problem is that We do not how to find out an engineer (or a team of engineers) who is excellent at qualification, skill and mindset. The best way for We is to ask IT recruitment agencies for support.


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