Optimize Scala Workflows on Databricks

I have several Databricks notebooks written in Scala that are beginning to show their limits as data volumes grow. I need an experienced hand to dive into the code, refactor any bottlenecks, and apply best-practice Spark tuning so the jobs run faster and more reliably.

Beyond pure optimization, I also want these notebooks to connect cleanly with the rest of our stack—think scheduled triggers, downstream analytics tools, or external APIs—so seamless integration work is part of the brief. If you have ideas on how to streamline those hand-offs, I’m eager to hear them.

Deliverables
• Revised Scala notebooks (or .scala files) with performance improvements clearly commented
• Integration scripts or connectors that plug Databricks into the specified external tools
• A short read-me summarizing changes, cluster settings used, and any follow-up actions I should take

If you’ve tuned Spark jobs before, profiled Scala code inside Databricks, and handled tool integrations without breaking-changes, this assignment should be straightforward. I’m ready to start as soon as we agree on the approach and timeline.