3 Skills of Highly Effective Data Engineers— Part 2
Reflecting on skills that proved essential for my career growth
This article follows Part 1 on the key factors that can be utilized to level up as a Data Engineer. You can find it here
In this part, we will dive into three more important aspects. These revolve around sensitive data handling, Machine Learning & AI, and keeping up with the latest trends. Let’s dive in
Sensitive Data Handling
Achieving one of the first milestones in data engineering ensures making data readily accessible in a quick and timely manner for business users. These efforts, particularly in the initial stages, introduce layers of technical debt. Some aspects of technical debt are acceptable in the short term. However, when tackling sensitive data, this leaves an organization suspectable to legal actions and an elevated risk of data breaches.
Familiarity with Data Privacy Laws and Regulations: A basic understanding of data protection laws and regulations can significantly reduce the time engineers spend on tackling or integrating personal customer data into their data platforms.
Key regulations include the GDPR (General Data Protection Regulation), an EU regulation for the data protection and privacy of EU residents. The CCPA (California Consumer Privacy Act), is a US state law to protect the data and privacy rights of Californian residents. Awareness of such regulations helps engineers gain competitive advantages, especially in critical organizational domains where handling personal data carefully and securely is considered of upmost importance.
Privacy by design: Understanding laws and regulations is a first step, equally important is knowledge on how to implement the appropriate architecture and integrations to protect such sensitive data. This approach not only ensures the right identification of personal data by also the correct management of it. If you are interested in reading more on strategies to tackle sensitive data handling, feel free to check my article Architectural Strategies for Handling Sensitive Data
Machine Learning & AI
Basic Knowledge: It cannot be stressed enough the impact AI is having on the data industry. As data engineers, we are at the forefront of supplying data for AI models to consume. Basic knowledge in Machine Learning & AI helps engineers on one hand to deliver efficiently the appropriate data and structure to consumers, and on another to utilize those systems for their own engineering use cases. The increasing adoption of AI by organizations gives a clear indication that data engineers who are adept in implementing and maintaining infrastructures that support AI occupy the top essential skills hierarchy.
Informed about Latest Industry Practices
The third and last tip is staying up to date with the latest industry practices and trends. This does not necessarily indicate that new trends should be instantly adopted, but rather to keep an eye on the data sector and the technological advancement, potentially having an impact. This involves reading books (feel free to check here the 4 Top Data Engineering Books ), blog articles (if you’re reading this, you’re already on the right track here), podcasts, etc. The technological advancement is moving fast and staying up to date with it is more important than ever.
Final Words
In conclusion, I hope the provided list meets your needs to excel further and supports your learning paths on the Data Engineering track. If you have any further recommendations or additional resources that you believe should be included, please feel free to add those in your responses.
Happy Engineering!
