Modern Data Stack Design

The challenge

A scaling technology business needed control over its data estate. Important operational data lived across SaaS tools, APIs, databases, spreadsheets, and internal systems. Reporting was hard to trust, definitions were inconsistent, and teams spent too long reconciling numbers by hand.

They needed a platform that could support reporting, analytics, automation, and future AI use cases — without immediately hiring a large data team.

What we did

• Mapped the key source systems and the most important business domains
• Designed ingestion patterns for SaaS tools, APIs, databases, SFTP files, and static uploads
• Structured data into raw, transformed, and business-ready layers
• Defined a warehouse model around common domains — sales, customer, marketing, finance, and operations
• Introduced data contracts, metric definitions, and clear ownership principles
• Designed orchestration so pipelines and transformations run reliably
• Set a platform direction able to support AI agents, semantic search, and trusted business Q&A later

Why it matters

Scaling companies often reach a point where spreadsheets, SaaS reports, and isolated dashboards stop being enough. A lean but well-designed data foundation improves trust, reduces manual effort, and gives the business a platform for better decisions.