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Prompt course · 8 lessons · 29 prompts · 1 hour · Beginner

AI for Data Engineers

This prompt course teaches data engineers to use AI for transformations, quality checks, pipeline design, monitoring, query tuning, storage, automation, and documentation. Each lesson gives you prompts you can adapt to your own pipelines and warehouse.

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What you'll learn

  • Data Transformation: Ask AI to draft and debug code that cleans raw data into analysis-ready tables.
  • Quality Checks: Design checks that catch missing values, duplicates, and broken rules before analysts see them.
  • Pipeline Design: Plan pipeline steps, choose batch or streaming, and document the architecture with AI help.
  • Pipeline Monitoring: Use AI to read logs, find likely causes, and suggest fixes for failed pipeline runs.
  • Query Tuning: Optimize slow SQL, test indexing ideas, and explain execution plans in plain language.
  • Storage and Schema: Compare storage formats, choose partitioning, and design schemas for your warehouse.
  • Workflow Automation: Write orchestration code and schedule recurring pipeline tasks with AI as a drafting partner.
  • Team Documentation: Explain data models to analysts, write data dictionaries, and draft handoff notes.

What's inside

8 lessons · 29 prompts
  1. Before you start · framework course Context Engineering and Structured PromptsFor data engineers, context engineering structures prompts so agents generate dbt models, validate schemas, and automate pipeline checks from warehouse context.
  2. Start here Priya's Thursday, two waysA day in the life of a Data Engineer, before and after these prompts.
  3. 01 Lesson 1 · 3 prompts Data Transformation
  4. 02 Lesson 2 · 3 prompts Data Quality Checks
  5. 03 Lesson 3 · 3 prompts ETL Pipeline Design
  6. 04 Lesson 4 · 4 prompts Pipeline Monitoring & Troubleshooting
  7. 05 Lesson 5 · 7 prompts Query Performance Tuning
  8. 06 Lesson 6 · 3 prompts Data Storage & Schema
  9. 07 Lesson 7 · 3 prompts Workflow Automation
  10. 08 Lesson 8 · 3 prompts Collaboration & Documentation

About this course

7 topics

Practical Prompts for Data Engineering Work

Data engineers juggle code, data checks, pipelines, and questions from analysts. This course turns those tasks into clear prompts you can use with ChatGPT, Claude, or Gemini.

You will learn how to ask for transformation code, quality checks, pipeline designs, log explanations, query tuning, schema choices, automation, and documentation. Each lesson builds on the one before it.

  1. The lessons
    1. Data Transformation: Use AI to write and debug code that cleans and transforms raw data into analysis-ready datasets.
    2. Data Quality Checks: Use AI to design and implement data quality checks that catch issues before they reach analysts.
    3. ETL Pipeline Design: Use AI to design ETL pipelines, choose the right approach, and document the architecture.
    4. Pipeline Monitoring & Troubleshooting: Use AI to diagnose pipeline failures, interpret logs, and get suggestions for fixes.
    5. Query Performance Tuning: Use AI to optimize slow SQL queries, suggest indexing, and explain execution plans.
    6. Data Storage & Schema: Use AI to design schemas, choose partitioning, and compare storage formats for your data warehouse.
    7. Workflow Automation: Use AI to write orchestration code, automate recurring tasks, and schedule pipelines.
    8. Collaboration & Documentation: Use AI to explain data models to analysts, write data dictionaries, and draft documentation for handoffs.
  2. What This Course Covers

    The course walks through eight lessons that match common data engineering tasks. You start with transformations and quality checks, then move into pipeline design, monitoring, query tuning, storage, automation, and documentation.

  3. How the Lessons Connect

    Each lesson uses the same pattern: a real task, a prompt you can copy, and a way to check the answer. Later lessons reuse earlier ideas, so pipeline design builds on clean data and quality checks.

  4. How to Use Prompts Well

    Give the AI context: your source tables, column names, business rules, and the tool you use. Ask for small steps, request explanations, and always test the output before it touches production.

  5. Who This Course Is For

    This course is for data engineers who build pipelines, manage warehouses, or support analysts and data scientists. It works whether you use SQL, Python, Spark, or a mix of tools.

  6. Safety and Privacy

    Do not paste secrets, credentials, customer records, or private data into an AI tool. Follow your company rules, use approved tools, and share only the details needed to get help.

  7. Your Next Step

    Pick one task from your week, such as a slow query or a failed pipeline, and try the matching prompt. Then continue through the lessons and keep the prompts that save you the most time.