Course overview
Start hereAI for Data Architects
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Priya's Wednesday, Two Ways

9 lessons · 25 prompts

A day in the life of a Data Architect: what changes with these prompts.

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Priya, a data architect at a health insurance company.

Priya starts her Wednesday with a request from the claims team. They need a new data model for a wellness program. She opens ChatGPT and uses the "Drafting Data Models" prompt. She types the business rules, the existing claims tables, and the reporting needs. In a few minutes, she has a first-pass schema and a list of questions to ask the claims manager.

Next, she has to write a decision record about moving a reporting database to a new platform. She uses the "Selecting Databases And Platforms" prompt in Claude. She pastes the requirements, the cost notes, and the team's skills. The AI gives her a comparison table and a draft summary. Priya edits it, adds her own judgment about vendor support, and sends it to the architecture review group.

After lunch, a pipeline between the CRM and the data warehouse is failing. She uses the "Mapping Data Flows And Integrations" prompt in Gemini. She describes the source fields, the target fields, and the transformation rules. The AI helps her write a field-level mapping and a short note about what changed. She shares it with the engineer who owns the pipeline.

By 4 p.m., Priya has finished the model draft, the platform decision record, and the integration note. She uses the time she saved to walk through the model with the claims manager and to review a junior architect's work. She leaves on time, knowing the details are documented and the team can move forward.

Before

  • Starting from blank pages every time
  • Documentation piles up behind the design work
  • Stakeholders wait too long for answers
  • Evenings spent rewriting the same explanations

After this course

  • First drafts ready in minutes
  • More time for judgment and trade-offs
  • Clear documents that teams can follow
  • Leaving work at work

What you'll learn

  • Draft Data Models: Turn business requirements into first-pass data models, DDL, and schema reviews with AI as a drafting partner.
  • Write Architecture Docs: Produce decision records, data flow narratives, and standards text that explain your architecture clearly and consistently.
  • Define Governance Rules: Draft classification, retention, and domain-specific governance policies that teams can actually follow.
  • Map Data Flows: Document field-level mappings, pipeline behavior, and interface contracts between systems.
  • Select Platforms: Compare database and platform options against requirements and turn the analysis into a decision-ready summary.
  • Optimize And Evaluate: Get concrete ideas for slow queries, partitioning, and capacity, and assess unfamiliar tools with a proof-of-concept plan.
  • Explain To Stakeholders: Translate technical architecture into business language and prepare for the questions and objections you will face.
  • Collaborate With Engineering: Convert architecture into buildable work and safe migration steps that engineering teams can execute.

How this course works

  1. 9 lessonsOne task of your job each, from drafting data models to collaborating with engineering teams.
  2. Ready-to-paste promptsCopy, fill in the parts in {{brackets}}, paste into ChatGPT, Claude or Gemini.
  3. Tick and completeTick the prompts you tried and mark each lesson complete.
  4. Get certifiedFinish and keep the prompts as your own library.