Azure Automation Use Case

Normally for short living jobs I'd say use an Azure Function. Thing is, they have a maximum runtime of 10 minutes unless you put them on an App Service Plan. But that will costs more unless you manually stop/start the app service plan.

If you can containerize the whole thing I recommend using Azure Container Instances because you then only pay for what you actual use. You can use an Azure Function to start the container, based on an http request, timer or something like that.

You can set a restart policy to indicate what should happen in case of unexpected failures, see the docs.

Configuration can be passed from the Azure Function to the container instance or you could leverage the Azure App Configuration service.

Though I don't know all the details, this sounds like a good candidate for Azure Batch. There is no additional charge for using Batch. You only pay for the underlying resources consumed, such as the virtual machines, storage, and networking. Batch works well with intrinsically parallel (also known as "embarrassingly parallel") workloads.

The following high-level workflow is typical of nearly all applications and services that use the Batch service for processing parallel workloads:

Basic Workflow

• Upload the data files that you want to process to an Azure Storage account. Batch includes built-in support for accessing Azure Blob storage, and your tasks can download these files to compute nodes when the tasks are run. Upload the application files that your tasks will run. These files can be binaries or scripts and their dependencies, and are executed by the tasks in your jobs. Your tasks can download these files from your Storage account, or you can use the application packages feature of Batch for application management and deployment.
• Create a pool of compute nodes. When you create a pool, you specify the number of compute nodes for the pool, their size, and the operating system. When each task in your job runs, it's assigned to execute on one of the nodes in your pool.
• Create a job. A job manages a collection of tasks. You associate each job to a specific pool where that job's tasks will run.
• Add tasks to the job. Each task runs the application or script that you uploaded to process the data files it downloads from your Storage account. As each task completes, it can upload its output to Azure Storage.
• Monitor job progress and retrieve the task output from Azure Storage.

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I would go with Azure Devops and a custom agent pool. This agent pool could include some virtual machines (maybe only one) with docker installed. I would then install all the necessary packages that you mentioned on this docker container and also the DevOps agent (it will be needed to communicate with the agent pool).

You could pass every parameter needed in the build container agents through Azure Devops tasks and also have a common storage layer for build and release pipeline. This way you could mamipulate/process your files on the build pipeline and then using the same folder create a task on the release pipeline to export/upload those files somewhere.

As this script should run many times through the month, you could have many containers so that to run more than one job at a given time.

I follow the same procedure for a corporate environment. I keep a VM running windows with multiple docker machines to compile diferent code frameworks. Each container includes different tools and is registered to a custom agent pool. Jobs are distributed across those containers and build and release pipelines integrate with multiple processing.

You probably suppose to use Azure Data Factory for moving and transforming data. Then you can also use ADF for calling Azure Batch that will be using python. https://docs.microsoft.com/en-us/azure/batch/tutorial-run-python-batch-azure-data-factory

Adding more info could probably suggest other better suggestions.