Who is a Big Data Engineer: Roles, Responsibilities, Skills and Qualifications

The growing role of big data in market competitiveness and organizational augmentation is forcing businesses to seek the best talents to head their big data initiatives.

For one, we’re generating data at unprecedented levels. According to itransition.com, we’re on track to generate 175 zettabytes of data by 2025. By comparison, the total data generated by 2013 was only 4.4 zettabytes.

Secondly, the number of people and devices connected to the internet is also snowballing. Currently, only about 47% of the global population is connected to the internet. This figure will jump to a staggering 75% by 2025.

Above all, the accelerated proliferation of the Internet of Things (IoT) means that we’re adding a record number of computer-enabled devices to the market every day. In 2020, there were 26.66 billion IoT devices, compared to only 7 billion in 2018.

Collecting, breaking down, and extracting useful patterns from such vast volumes of data requires a special kind of data scientist. And that’s where the Big Data engineer comes in handy.

Who is a Big Data Engineer?

A Big Data engineer is a professional who creates and manages an organization’s Big Data infrastructure and tools. Their primary responsibility is to get results from vast amounts of data quickly.

The Big Data Engineer builds on the Big Data solutions the data architect has designed and is expected to create, maintain, test, and evaluate Big Data solutions. They also spend a significant portion of their time developing solutions because of the experience they have with Hadoop-based technologies, such as MapReduce, Cassandra, and Hive MongoDB.

In short, Big Data engineers are experts who build large-scale data warehousing solutions. They are familiar and regularly work with the latest NoSQL database technologies.

Big Data Engineer vs. Data Engineer?

Before we look at the roles, responsibilities, and qualifications of the Big Data engineer, it’s essential to understand the distinction between a Big Data engineer and a data engineer.

In general, the data engineer extracts data from different sources, transforms it into usable formats and loads it into a central repository, also known as a data warehouse. From the data warehouse, the data is sent to data scientists for further analysis.

Big Data engineers do almost the same things but at a much higher level. As such, the tools, processes, and even skill jump from the data engineer to Big Data engineer role is massive. The following are some of the highlights;

• A data lake replaces the data warehouse: While both data warehouses and data lakes serve as data containers, they aren’t the same. Data warehouses are large repositories of structured, filtered data that has already been processed for a specific purpose. Data lakes are even larger, usually the size of several data warehousing. Moreover, they mostly comprise raw, unstructured data, the purpose for which is not yet defined.
• One works with structured data, the other with unstructured data: Data engineers work with structured data. This comprises clearly defined data types whose patterns make them easily searchable. Unstructured data is “everything else.” It consists of data in unsearchable formats, including social media posts, podcasts, and YouTube videos.
• A greater duty to prevent data swamps: Since the data in warehouses is already structured, there’s minimal risk of the data “going bad.” That’s not the case with data lakes. Given that the data in these lakes is mostly unstructured, it can easily mutate into something else. When this happens, the lake can transform into a swamp.

Roles and Responsibilities of the Big Data Engineer

Big Data engineers have a responsibility to design a Big Data platform’s architecture, maintain the data pipeline, structure data, customize and manage the relevant data tools and analytical systems, and create data access channels for data scientists.

But, their primary duties lie in two areas, according to one of the biggest job posting platforms in the world, Glassdoor.com.

• Performance optimization

Performance optimization means applying the necessary infrastructure and tools to speed up the data query process. Two critical processes involved here are developing database query techniques and ensuring efficient data ingestion.

With regard to data query techniques, the Big Data engineer needs to work on data partitioning (break down and storage of data in independent subsets). Typically, each unique set of data gets a separate partition. The engineers also handle database indexing, which involves structuring data to speed up retrieval in large tables.

Efficient data ingestion is mostly about discovering patterns in large data sets using various data mining techniques and data ingestion APIs.

• Stream processing 

Stream processing refers to a computer programming architecture where data is computed directly as it is produced or received. It is a Big Data technology that focuses on real-time processing of continuous flows of data streams in motion.

Big Data engineers are responsible for designing streaming data architecture that automatically collects data, delivers it to each actor, and ensures that they run in the correct order. An excellent streaming architecture also collects results, scales for higher volumes, and handles failures.

The engineer also has a duty to design and manage the architecture for concurrent processing of multiple streams.

Skills and Qualifications

A Big Data engineer has sufficient exposure and background in software engineering. In particular, they are experts in object-oriented design, coding, and testing patterns. They are also familiar with engineering (commercial and open-source) software platforms and large scale data infrastructures. If you’re hiring one, focus on the following;

• Expertise in data processing frameworks:  There are three broad categories of data processing frameworks, i.e., batch-only, stream-only, and Hybrid. All three are Apache-based.
• Hadoop proficiency: Of all the specific data processing frameworks, many employers look for proficiency in Hadoop. It is much easier and less expensive to implement.
• Mastery of real-time processing networks: Many organizations implementing Big Data would also benefit immensely from an expert in real-time processing. Therefore, a Big Data engineer with experience in Kafka, YARN, and Samza API is a huge plus.
• NoSQL mastery: Besides the Big Data frameworks, the engineer must also be skilled in NoSQL database technologies. These include HBase, Cassandra, and MongoDB.
• Machine Learning (ML) expertise: Finally, a consummate Big Data engineer is also competent in at least a few machine learning platforms. H2O and Mahout are the most popular ML platforms for Big Data integration.

About NIX Solutions

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