What do you do if your data engineering skills are becoming obsolete in an automated world?

Acknowledge the evolving landscape of technology and data engineering. Embrace change as an opportunity for growth and innovation rather than a threat to your skillset.

With the buzz of Generative AI, we need to acknowledge that automation specially around AI agents will become the new norm. Think about the data engineering areas that are essential to power up AI. Invest your time into learning techniques around data quality, data prep, data lineage that help you generate and maintain high quality data that is crucial for AI use cases.

To stay relevant in an automated world, update skills by learning emerging technologies like cloud computing, AI, and machine learning. Upskill in programming languages, data processing frameworks, and automation tools. Embrace continuous learning through online courses, workshops, and hands-on projects. Additionally, seek mentorship, join communities, and contribute to open-source projects to stay abreast of industry trends and maintain relevance in the data engineering field.

In tech is a ever evolving landscape and data engineering the process that was there and the tolls that were there 10 years ago are currently not used nowadays,upskilling is a neccesity in this field learn modern tools like if you were using the traditional ETL change and use ELT as this now improves scalability and saves on cost .Also cloud services such as aws and azure are becoming quite common

Continuously update and expand your data engineering skills to align with emerging technologies, tools, and methodologies. Stay abreast of industry trends and advancements through online courses, workshops, and professional development opportunities.

Understanding the fundamentals is very crucial while stepping into AI/ML field. Even if the learning curve might be steep compared to other fields and slower as well, taking as much time in the basics of math and statistics will help in excelling in the long run.

Definitely learning AI/ML is a no brainer ,AI should not be seen as an enemy or something taht will replace you rather it should be used to improve productivity ,speed and quality ,AI is the future but you can make it your strength or your worst nightmare ,also learning to use it efficiently is a must to ensure data integrity.Learning ml models is also fun i have been creating these models for a while now and although ml models is not just about coding for me its a skill that will be needed in the long run

Explore the realms of artificial intelligence (AI) and machine learning (ML) to augment your data engineering expertise. Acquire foundational knowledge in AI/ML concepts, algorithms, and frameworks to leverage automation and advanced analytics capabilities in your work.

Well network is your networth ,connect with people that are knowledgable with these new trends in data engneering and learn from them how they are coping with these industry trends such as automation and makes earning and collaboration in the future very easy

Expand your professional network within the data engineering community and beyond. Engage with peers, thought leaders, and industry experts through conferences, meetups, online forums, and social media platforms to exchange ideas, insights, and best practices.

Share your knowledge, experiences, and contributions openly with the data engineering community. Contribute to open-source projects, publish articles or blog posts, and participate in collaborative initiatives to showcase your expertise and establish yourself as a thought leader.

This is going to be increasingly relevant in the upcoming years. The below tips might help: - Learn how to use AI in Data Engineering work. Expand skill set to include relevant automation tools and platforms. - Continuously learn and adapt by taking courses in emerging technologies and methodologies. - Engage in projects that involve new technologies to gain hands-on experience. - Network with professionals and join communities to stay informed about industry trends. - Contribute to open-source projects to apply new skills in real-world scenarios. - Seek mentorship or guidance from experts to navigate career transitions effectively. - Embrace a mindset of lifelong learning, recognizing that technology evolution is constant.