Keep in mind that most analytics roles are a move down from most software developer jobs in terms of technical skills required, salary, prestige, job satisfaction. The last part is obviously my personal opinion - my background is in data science and early on in my career I could leaned more towards analytics or more towards software engineering and I very quickly figured out that I strongly prefer the engineering jobs (whether it's ML engineering, or even non-data science- or ML-related software development). Right now I'm data scientist in title, with an MLE job description and responsibilites, and I'd be happy to do either MLE or backend development in my next job. Analytics and data engineering are positions that I actively avoid. But again, that's just me, plenty of people like these roles.
On the technical side we have things like Apple's Intelligent Tracking Prevention, App Tracking Transparency, Mail Tracking Transparency and Link Tracking Protection, Chrome is finally about to phase out 3rd party cookies and browsers like Firefox and Edge lock down or block common web tracking technologies. - just to name a few. All of this combines to add noise, or a reduction in volume which impacts analysis efforts.
On the regulation side, we have global laws such as Europe's GDPR, 4 US States with current data privacy laws in effect, and a total of 12 US slated to take effect before 2026 - all of which are slightly different in impact scope. This results in scenarios that even if you technically can collect data, you may not be legally allowed to, or, alternatively prevented from using it in specific ways.
This will force companies to switch back to Media Mix Models for attribution and adopt new privacy focused solutions for existing use cases to some extent.
Data analytics is getting more complex, not less. However that results in a massive amount of needed learning on a continued basis - which may not be for everyone.