I want to pull sales and inventory data straight out of my restaurant’s POS/back-office system, flow it into a clean data store, and surface everything on a web dashboard that highlights sales trends at a glance. From there, those insights should automatically drive smarter ad spend and content decisions across my social channels and other digital campaign outlets. Here’s how I picture the collaboration: • Data engineering – set up secure extraction (API, webhook, or flat-file) for both sales and inventory tables, build a lightweight ETL in Python/SQL and park the clean data in a cloud database that I can own. • Analytics layer – create a web-based dashboard (Power BI, Tableau, or a custom React/Vue front end) focused on sales-trend visualisations with drill-downs by daypart, item, and location. • Marketing intelligence – translate the dashboard signals into rules or ML models that automatically allocate budget, schedule posts, and optimise creatives through the Facebook/Instagram, Google Ads, and any other relevant APIs. I’m aiming for true closed-loop learning: each campaign result feeds back into the model to refine the next round of spend. Acceptance criteria 1. Data pipeline updates at least hourly with no manual intervention. 2. Web dashboard loads in <3 seconds on a standard browser and clearly surfaces sales trends. 3. Automated campaigns demonstrate that parameter changes (budget, audience, timing) are driven by the metrics coming from the dashboard, and I can view a log of each decision. If this end-to-end flow sounds like something you’ve built before, let’s talk details and milestones.