How has the Need for Data Engineers Evolved Over Time?

Data engineers have a great opportunity to own the change toward treating data as a product

The area of data analytics has a promising future. Traditionally, businesses have focused on data collection and visualisation. Teams are beginning to investigate new approaches to convert, manage, and assess their data, now that these concerns have been highlighted thoroughly. In the next phase of their data analytics journey, companies must reframe their goals and organisational needs. Efficiency, flexibility, and accessibility are significant pillars for the majority of data engineers polled. The diversity of the viewpoints of those interviewed about the future of space was particularly remarkable in this research. Everything from broadcasting to cataloging to spy has been identified as potential themes that may grow more significant in the next five years, according to the teams.

For example, Maxime Beauchemin is a name that requires no introduction to the field of data engineering. He was one of the first data engineers at Facebook and Airbnb, and he built and open-sourced Apache Airflow, a widely used orchestrator, followed by Apache Superset, a data exploration tool that's sweeping the data visualisation scene. Maxime is currently the CEO and co-founder of Preset, a fast-growing firm that is pioneering AI-powered data visualisation for modern businesses. Maxime is widely credited with experiencing – and even architecting – many of the most influential data engineering technologies of the previous decade, as well as pioneering the profession itself in his landmark 2017 blog article, The Rise of the Data Engineer, which records many of his findings. In brief, Maxime claims that teams require a dedicated engineer to handle ETL, construct pipelines, and grow data infrastructure in order to properly scale data science and analytics. The data engineer has arrived.

Data Science interviews increased by 10% in 2020, according to interviewquery.com's 2021 Data Science Interview Report, which analysed over 10,000 Data Science interview experiences. Data Engineering interviews increased by 40% in 2020. Every day, more data is created, and this trend will continue. Engineers to manage data will be in higher demand as the amount of data grows exponentially. Another indicator is the growing demand for big data engineering services from consulting organisations such as Accenture and other IT businesses such as Cognizant. As the amount of data grows, so does the demand for data engineering services. The market for data engineering services is growing at 18 percent per year and is predicted to reach 31 percent per year by 2025.

Within the data team, specialisation will increase

Today's data engineers and analysts sport a variety of jobs. This is because the data team's budget has just lately been boosted. Data teams will specialise to focus on a certain function when the usefulness of data teams becomes more apparent and more investment is made in this sector. A reliability data engineer, a visualisation lead, and a separation between the backend and frontend data engineering teams may be required. We predict that over the next five years, these kinds of organisational reforms will begin to take shape.

The "data divide" between data providers and users will close

The distance between data consumers and data producers will continue to close as more money is invested in self-service analytics. All data teams will be required to use tools that assist them consolidate their understanding of data. We've solved data storage, data movement, and data visualisation. The notion of self-serve analytics and knowledge is the second major concern when we look at the issues that a team confronts today.

Data will be turned into a product

More data teams will embrace product-like techniques for measuring, managing, and developing data. On the surface, this appears to be a move toward more agile project management. On a more complex level, this may imply moving toward data technologies that facilitate cross-organizational cooperation, version control, and monitoring. We believe that data analytics will be an exciting field for innovation.

Data engineers earn a lot of money in many situations, even higher than data scientists'. Many compensation analyses have indicated that Data Engineers are among the highest-paid professionals, with no signs of slowing down in the near future. A recent study shows that Data Engineers are in limited supply. As a result, candidates are far more likely to negotiate a larger wage range throughout the recruiting process. It goes without saying that everyone's recruiting process is unique, and the bargaining range is determined by a variety of circumstances like experience, skills, location of company, previous achievements, industry, etc. Data Engineers are responsible for the essential basis of how the company's data is structured in the data warehouse. To provide a scalable, high-performance infrastructure to serve all of the company's data demands, tasks span from creating table architectures to implementing ETL/ELT pipelines.