An Essential Guide to Building a Career as a Data Acquisition Engineer

The market for Data acquisition systems or DAS is among the fastest-growing ones in the world, generating trillions of dollars every year for Telecom, Healthcare, and Online retail/ e-commerce companies. Let’s discuss the scope of data acquisition solutions in 2022 and the kind of expertise one requires to master in this erudite field.

Faster internets, superior mobile connectivity, zero switching cost to 5G and IoT devices, and advancement in Ethernet technologies have allowed other emerging next-generation capabilities to rise from the shades of darkness within a very short span of time. Today, we are looking at tons and tons of data every day originating from every single interaction that we have online. Whether it’s chatting with our friends and colleagues, signing up for a newsletter, watching an Instagram or TikTok reel, or simply scrolling through our daily dose of news feeds – everything is driven by the powerful world of data science. When we are working with such a huge volume of data it becomes extremely important for data analysts to ensure there is compliance associated with the process, and that’s why we are witnessing the promotion of “data acquisition systems” or DAS or DAQ solutions in Data Science and Machine Learning industry.

Who is a Data Acquisition Engineer?

A data acquisition engineer is a qualified data engineer or analyst who functions as part of a Data Operations team. An engineer with a data acquisition job title often reports to a data scientist or senior DataOps manager to fulfill the daily needs arising from working in world class AI and machine learning development environment. As a DAE, you would be entrusted with the responsibility to not just tackle challenges in the strategic acquisition of high quality data but also ensure that this data remains validated for a range of other data ops applications such as Natural Language Processing or NLP, Computer vision, Biometric classification, and so on.

In a large sized AI ML development team, you might get to work with at least 2 DAEs, who could be collecting and validating data from different sources. While one set of data acquisition tools cleanses historical data, the other works extensively on real time data collection and processing tools to create batch or streams of high quality data for business intelligence and analytics purposes.

What does it take to become a Data Acquisition Engineer?

The first step to becoming a data acquisition engineer starts with a degree or diploma in Engineering or a Master’s in Computer Applications (CA). Most IT engineers switch to data acquisition roles after pursuing a certification in Python coding and R programming, enabling them with superior skills in data management and analytics.

Data acquisition engineers can further expand their data science career options by mastering their expertise in statistical analysis, sentimental analysis, computer vision engineering, NoSQL DBS, web development, and Cloud project management using AI Ops and Auto ML techniques. 

How Much Time Does It Take To Become A Data Acquisition Engineer?

There is no solid timeline available in the market that can suggest it takes ‘X’ number of years to qualify as a data acquisition engineer. With a data science certification after graduation, you can start working in DataOps projects, and interview specifically for this role to gain a competitive edge in the industry that is definitely looking great for DAE profiles. 

Which Industries Hire The Most Number Of DAEs?

A domain that hardly makes up for less than 1% of total cost and resources, generates more than 70% of the revenue in the data science industry. Between 2021 and 2025, this sector is slated to grow at more than 10% per year, bringing in more than 5 billion dollars in revenue every year through mergers and acquisitions, funding rounds, and IPOs. Nonetheless, if you specifically want to target industry for DAE jobs, here are some of the biggest destinations. 

• Industrial IT automation and Ethernet networking 
• IoT and Connected Mobility
• Eye-tracking devices and wearables
• Self-driving cars
• Healthcare services and clinical research
• Drug development
• Cloud software and Mobile application development 

From smart manufacturing platforms to world-class mobile internet connectivity solutions, the role of Data Acquisition is only growing to grow larger.