I'm going to disagree heavily here. The world of cloud computing, microservices, and hosted/managed services has made the analyst and data engineers job easier than ever. If the software team builds a new dynamodb table, they simple give the AWS account for the analytics team the appropriate IAM permissions and the analytics team will set-up an off-peak bulk extract. A single analyst can easily run an entire data warehouse and analytics pipeline basically part time without a single server using hosted services and microservices. With a team of analysts, the load sharing should be such that the ETL infrastructure is only touched when adding new pipelines or a new feature transformation.

And for data scientists working on production models used within production software, most inference is packaged as containers in something like ECS or Fargate which are then scaled up and down automatically. Eg, they are basically running a microservice for the software teams to consume.

Real time reporting, in my opinion, is not the domain of analysts; it's the domain of the software team. For one, it's rarely useful outside of something like a NOC (or similar control room areas) and should be considered a software feature of that control room. If real-time has to be on the analysts (been there), then the software team should dual publish their transactions to kinesis firehouse and the analytics team can take it from there.

Of course, all of this relies heavily on buy-in to the world of cloud computing. Come on in, we all float down here.