Big data means big business.
From a broader perspective - everything is data-oriented nowadays. No matter if you're a large corporation with dozens of clients, or a small start-up looking to propel its product or services beyond competition, the larger portion of the way businesses operate revolves around collecting, analyzing, processing, and interpreting data.
Keep reading to find out more about how a Big Data Engineer can improve your business results and why you need a Big Data pro in your team.
Why is there a Big Data talent crunch on the market?
The Great Resignation is causing a talent crunch everywhere. And the fierce tech talent battle has no end in sight. At the same time, the remote revolution made a lot of companies become digitally savvy faster than they expected. Now businesses, big or small, are looking to hire proficient tech talent, and Big Data Engineers are no exception.
But let’s get back to the why. Here are some ideas that deserve further exploring:
- More companies are working with data. The main reason for the shortage of data engineers in the industry is because businesses are leveraging the value of big data in order to make an informed business decision.
- A fast-paced upskilling is required. Businesses are looking for data engineers to be skilled with up-to-date tech stack and skills, from a basic understanding of Python (Pandas, Numpy, and PySpark) and Scala programming languages to a strong grasp of SQL and Cloud (Azure or AWS) tools.
- Companies are hiring based on portfolios rather than degrees. Therefore, data engineers should create personal projects in order to make their portfolios competitive.
- The courses aren’t self sufficient. Learning from online courses isn’t sufficient for data engineers to land a job.
- Interviews for Big Data specialists are exhausting. With data engineering jobs getting more competitive, data engineer interviews are becoming one of the hardest to crack.
Big data will empower better business intelligence
In this day and age, data is the new oil. If you are serious about becoming a data-centric company then dealing with data is the most important step. Surveys report that by 2023, data scientists and analysts will lose up to 70% of their productive time to activities like searching, finding, interpreting, integrating, and sharing datasets, making data engineering experts the new power players on their teams.
No, don’t get me wrong. I’m not trying to downplay the importance of Data Scientists and Analysts in your company. What I’m trying to say is that the real power of data comes when science is coupled with engineering expertise. Big Data benefits the business’s decision-making process. Backed by data, businesses can improve performance and the quality of their operations.
Data-driven organizations are quicker to create effective commercial strategies and become more profitable. In addition to that, insights from Big Data ingestion and processing can create new business opportunities and revenue streams by focusing on consumers’ real needs.
Hiring Big Data engineers will help you build high performing data science teams
When you build a data team to launch a project, aligning team members is the most complex challenge. Hiring an unnecessary role leads to tasks overlapping and reduced performance.
To avoid that, businesses will have to look more at improving the efficiency of their data science team, spreading the workload, and removing the productive time loss among data scientists and analysts. This is where a Big Data Engineering professional will come in handy.
Data Engineers are data science enablers. Rather than spend time and money on bringing more Data Scientists to the team, use those resources to hire Data Engineering experts. They will not only help your company obtain, process, clean, and integrate user data, but also allow data scientists to dig deeper into a problem and solve it effectively. Think of it as building a rocket to get to the moon. You don’t need more astronauts to do it. You need people who can build the spaceship.
So, let’s do a recap. If your business needs an A + player that will create dashboards, tables, pipelines, reports, APIs, and automation, hiring a data engineer is the most optimal and cost-effective solution for the long term. For A/B testing, machine learning research, and the design of artificial intelligence pipelines, recruiting a data scientist would be a better choice.
Big data will get bigger
According to LinkedIn’s emerging job report, the Data Engineer job was listed the 8th fastest-growing job, along with other data-oriented positions like Data Scientist (3rd) and AI Specialist (1st).
Small and medium-sized startups and enterprises don’t have the proper reason to hire data scientists simply because they don’t have enough data. Instead, they need a prolific data engineer to collect, store, and analyze data efficiently to build the company’s data infrastructure and create a complex data flow and databases.
The main point of becoming a data-driven enterprise is to use data expertise for growth. Hiring a data engineering talent will not only allow your company understand how your customers and business work, but it also will help you employ more techniques and technologies to grow faster, through:
- Increasing data pipeline;
- Managing data warehousing for scalable analytics;
- Building a real-time data platform;
- Ensuring data security;
- Automating data compliance and auditing;
- Helping streamline data science workflows;
- Adding value to product offerings;
- Building out customer lifecycle and retention models;
- Developing new data models for research, reporting & machine learning.
Hiring a big data engineer that will employ a big data strategy, will help your business to better understand your customers, improve marketing techniques, make personalization possible and leverage data alongside real-time market analysis to develop and deliver new and improved products and services.
But, be aware! With a business growth comes more duties of managing data, like data throughput, data analytics, real-time predictions, customer feedback, data security, data regulations, and compliance. Luckily, with a talent network like Index you can find a proficient Data Engineer, you can leave all those responsibilities to. It’s worth a shot.
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