Data science and engineering have become the new corporate-technological frontier from assisting Facebook to tag you in pictures to running the self-driving car industry. A Gartner post stated that by 2022, about 90 percent of all corporate stratagems would cite information as a vital business asset and analytics as an essential move towards competency. This growing popularity and essentiality of analytics have necessitated data specialists’ need, more specifically, data engineers.
Data engineers vs. data scientists
The key catalyst for boosting an organization’s transformation and digitization efforts is analytics and data. To achieve said goals, you will need to understand the role data engineers and scientists play in your company.
Modern analytics has found its poster child in data science. This broad field encompasses everything from Artificial Intelligence and Machine Learning to deploying models that are predictive in nature. While data science is important for your business, it won’t have the intended impact without the engineering aspect’s brilliance.
Data engineers are data science enablers. Their roles are built around obtaining data, cleaning, and integrating it throughout your organization. In short, they carry out all the upfront work that data scientists need to dig into a problem. These engineers are critical to your business as they increase the team’s efficiency and reduce production time by around 60 percent.
The data engineering roles
Data engineers can fall into three main categories based on what they do. These are:
Generalist
They’re usually found in small teams or organizations—these experts wear many hats in such a setting. From managing data to analyzing it, these generalists do it all and cater to every step in the data process.
1. Pipeline-centric
These specialists are found in mid-size companies. Pipeline-centric data engineers work with data scientists to make sense of the raw information collected.
2. Database-centric
This last group of data engineers is found in large companies. Here, managing the data flow is a full-time job. Database-centric engineers concentrate on analytics databases. They work with data warehouses on several databases, plus they develop table schemas.
The responsibilities of data engineers
These engineers have the job of organizing and managing data. They also ensure to keep an eye out for inconsistencies or trends that have a meaningful impact on your business objectives. These guys need all the technical expertise that comes with acquiring knowledge in mathematics, computer science, and programming fields. However, as they will need to communicate this information to colleagues and leaders in the organization, they need soft skills to coherently relay their findings.
Some everyday responsibilities among data engineers include:
- Alignment of the systems and data store architectures with business requirements
- Conducting research for business and industry queries
- Data acquisition
- Development, construction, testing, and maintenance of architectures
- Development of data set procedures
- Delivery of updates to the company stakeholders based on the analytics
- Deployment of complex analytics software, statistical, and machine learning methods
- Identification of subtle data patterns
- Identification of ways to improve data quality, reliability, and efficiency
- Preparation of data for prescriptive and predictive modeling
- Use of certain programming languages and their tools
The data engineer skillset
Data engineering is a very delicate field, in both its workings and its effects on your system. A competent and skilled engineering team is therefore vital to your organization’s digitalization. You may already know that these specialists ought to have working knowledge in programming languages such as Java and Python, and also SQL database design; the following data engineering skills are also just as crucial:
- Knowledge of Scala Programming Language
- Apache Spark
- Data modeling
- Data warehouse
- Should know their way around Linux
- Apache Hadoop
- Should be familiar with the workings of Amazon Web Services
- Big data analytics will also come into play
- Lastly, software development is another no-brainer
Hire us Today
Your data should be treated like the asset it is. Here at DevReady, we have data engineers who will improve your data science team’s efficiency and make their output better. Hiring our engineering team(s) will have your data platform built on the principles of reliability, scalability, and repeatability.
At the same time, you and other stakeholders will be able to focus on other aspects of your business. For all your data engineering problems, call us today and help us help you.
