Data Engineer Roles and Responsibilities

Nowadays, we are surrounded by data in our day-to-day life and it is creating an impact on our routine tasks. Over the last decade, several organizations have completed a digital transformation that leads to producing a huge volume of new types of complicated data at a high frequency. It shows us that software engineering wants an additional category to have data engineering. It is becoming very useful in several real-time platforms such as data storage, data mining, transportation, etc. Data engineering is related to analysis and tasks to collect and store data from different sources. Further, it processes and converts those data into clean one for the next level processes like data visualization, data science solution, business analytics, etc. So here data engineering makes data science more productive.

Data engineering roles (data engineers or big data engineers) are in high demand, according to a report, data engineering jobs grew 38% in 2019.  With the complete corporate digital transformation, the IoT ( Internet of Things), and the race to become AI-driven, almost all companies require data engineers to provide the foundation for successful and effective data science initiatives. So, the role of data engineers grows in importance and breadth and it is crystal clear now that data engineers are always in high demand. In fact, many of the companies are providing Data Engineer Bootcamp and training to produce skilled data engineers.

This article is a short introduction to what is a Data engineer and what is his role in an organization.

What is a Data Engineer?

A Data Engineer is a professional who works in a variety of settings to create systems that collect, manage, and convert raw data into useful information for business analysts and data scientists to interpret. They can make data accessible so that organizations can use it to optimize and evaluate their performance. Their primary goal is to prepare data for operational and analytical uses.

Data engineers are known as the data professionals or software engineers who are preparing big data infrastructure to be analyzed by data scientists. They design, create and integrate data from many different resources, and manage big data. They make data easily accessible and work smoothly by writing complex queries on that. Their main job role and goal in any organization is to optimize the performance of their company’s big data ecosystem.

Required skills for a data engineer are Hadoop, Hive, MapReduce, Pig, NoSQL, data streaming, SQL, Programming. They use several advanced tools such as Cassandra, MongoDB, MySQL, DashDB, etc. They focus more on the design and architecture of collected data because companies are finding more ways to benefit from data. Data engineers use data to understand the current state of the business, model their customers, predict the future, prevent threats, and create new types of products. It is witnessed that data is becoming more complex, so the role of data engineers will continue to grow in importance. As the demand for data is increasing, data engineering is becoming even more critical.

Roles and Responsibilities of Data Engineers

Data Engineers play a vital role in any organization for data-related operations and tasks. They are responsible for several complex data activities which are crucial for business growth. Data engineers role can be divided into three main categories such as:

• Data Engineer as Generalist- These types of data engineers are mostly found in small companies or small teams and wear many hats to focus on data-related issues. They are responsible for the data processes like data managing, analyzing, etc. It is a good role for data scientists who want to switch to the data engineer job role.

• Data Engineers as Pipeline-Centric- This role is suitable for midsize companies where these pipeline-centric engineers work with data scientists to help make use of the collected data. They required in-depth knowledge of computer science and distributed systems.

• Data Engineers as Database-centric- These database-centric engineers are suitable for large organizations where they manage the flow of data and focus on analytics databases. They work along with data warehouses across multiple databases. They are responsible for developing table schemes also.

 Some of the responsibilities of data engineers are mentioned below.

• Data engineers or analysts give answers to specific questions about data.
• Data engineers work on data architecture using a systematic approach to plan, create, and maintain the data properly according to business requirements.
• They develop, create, test, and maintain architectures.
• Data engineers acquire the data.
• They develop data processes.
• The very important task for data engineers is to collect data from the right sources. They formulate a set of dataset processes and optimize the stored data.
• The next responsibility of data engineers is to conduct research in the industry to find any issue that can arise while handling a business problem.
• Data engineers improve their skills to keep themselves updated with machine learning algorithms like the decision tree, random forest, K-means, and others. They don’t rely on theoretical database concepts alone. They also use Tableau, Apache Spark, Knime to generate useful insights for all types of businesses and industries.
• They use several tools and programming languages and identify ways to improve efficiency, reliability, and quality.
• Data engineers prepare data for predictive and prescriptive modeling.
• They deploy sophisticated analytics programs, machine learning, and statistical methods.
• They create reports and visualize them so that other people can understand and utilize the data more easily.
• They build models that predict which customers are likely to purchase a specific product.
• They use a descriptive data model for data aggregation to extract insights and find new patterns. They make predictive models also where they can apply forecasting techniques to learn about the future with valuable and actionable insights. So their aim is to identify hidden patterns from gathered data.
• Data engineers gather data requirements like how long data needs to be stored, how it will be used, and what systems and people need access to the data.
• Data Engineers automate tasks by diving into data and pinpoint tasks where human participation can be eliminated with automation.
• Data engineers deliver updates to stakeholders based on analytics.

With the above-mentioned lines, we can see how challenging and complex a role a data engineer is performing in any organization. That is why data engineers are becoming more and more important for all-size companies who want to make smooth and smart progress in their business with useful data insights.