Here's how you can safeguard your data engineering career in the era of automation.

To safeguard your data engineering career in the era of automation, focus on continuous learning and skill enhancement. Stay updated with the latest tools and technologies, such as cloud platforms, machine learning, and AI. Develop expertise in data architecture, pipeline automation, and big data frameworks. Emphasize problem-solving, critical thinking, and domain knowledge, which automation cannot replace. Collaborate effectively, and hone soft skills like communication and leadership. By combining technical proficiency with adaptability and strategic thinking, you can ensure your career remains resilient and valuable in an automated landscape.

Data engineering space is evolving rapidly. Everyone need to be very perfect with fundamentals. Most of the tools and frameworks are built on making the tools very intuitive to use. We need to keep an eye on these new enhancements and new products which will help us to stop reinventing wheel most of the team. Following the leaders in data engineering stream allows you to know what new things are coming up and give a way to what, where and how to learn them. Being part of the forum for each tech/frameworks gives visibility. Having strong fundamentals helps to upskill journey easy. Deep understanding helps in designing robust and scalable data products.

AI is inevitable companion :) Most of the times building data pipelines involves developing independent modules and glue them together. Leverage DevGPT, Codium likes AI tools to write the test cases, documentation and code coverage in your data pipeline components. We can provide code, SQL developed to these AI tools to make them more efficient and performant. Use AI to perform adaptive data quality checks, data agreements with data providing system, re route the flow depending on the external decision making systems.

AI is merely a tool to improve efficiency as a coder, which is a very specific form of a writer (a writer where precision is make or break to your outcome). Those who use AI and stay up to date with the latest developments will prosper since the efficiencies gained will become table stakes for any engineer. AI is not a replacement because the precision and general intelligence required to form complete solutions does not exist (ask again in decades time with AGI is upon us).

It's the intersection of technical and business skills that creates value from your data. You can write code more efficiently and think up strategies more quickly with AI. But operating an overall profitable business takes: critical thinking, adaptability, grit and a little bit of luck. Luck can be downplayed by those who work smarter and persevere for longer by looking deep within their datasets to steer the course.

It's been said that "People don't buy what you do but why you do it". This messaging requires a deep understanding of your own motivations, the human psyche and aligning this with your business. Once you have these, communicating it effectively to your audience takes time and refinement (by looking at your data) to deliver the message and grow your business. Speaking and writing with: honesty, depth and congruency is an innately human characteristic which can be honed.

Your networking can give you new knowledge to be prepared to new capacities and responsibility associated with new change in the fields by the automated activities on the field!

We always have a thought of Automation will take our jobs. But that’s not true. If we are doing the same work more than twice, then that work needs to be considered for automation. Eg: is unit testing. If we don’t do it, that will be a headache or repetitive work. So whatever needs to be automated, we should identify and do it. So that we can use that time for project improvement. Always there will be a room for improvement. So we should find out or spend time on that. In this way we will be using our time productively and efficiently. Instead of worrying about AI and automation, learn those tool and think, how can we potentially use it for our projects. Now this era is for AI to do automation, ours is to learn and think to use AI.

Upskill Continuously: Stay updated with the latest tools and technologies like cloud platforms, machine learning, and AI-driven data processing. Specialize: Develop expertise in niche areas like data security, real-time analytics, or big data solutions. Emphasize Problem-Solving: Hone your ability to solve complex, real-world problems that automation alone cannot address. Soft Skills: Enhance communication, collaboration, and project management skills to remain indispensable in cross-functional teams. Adapt and Innovate: Be proactive in adopting new methodologies and automating routine tasks, thus driving efficiency and innovation.