Training course · OpenAI Codex · Data Engineering

OpenAI Codex for Data Engineers

Delegate development work with OpenAI Codex: CLI and cloud agent, AGENTS.md, sandboxing, approvals and team PR workflows.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“OpenAI Codex for Data Engineers” is designed for roles such as Data engineers and analytics engineers, BI developers responsible for ETL and the data warehouse and Platform teams running lakehouse and streaming infrastructure and shows step by step how to use OpenAI Codex productively, safely and transparently in Data Engineering – from the fundamentals to advanced workflows.

OpenAI Codex is a coding agent that runs both locally, as a command-line tool and IDE extension, and as a cloud agent. Locally, Codex reads your repository, proposes changes and runs commands inside a sandboxed environment. In the cloud, it works on delegated tasks in parallel inside isolated containers and returns results as traceable diffs or pull requests. Over two days you will learn how to combine both modes effectively and how to scope tasks so that the agent can complete them reliably and with minimal back-and-forth.

Control and security sit at the heart of the course. With AGENTS.md you hand Codex project knowledge, build and test commands, and conventions. You configure approval modes and sandboxing so that the agent only does what you permit, and you set up cloud environments with suitable setup scripts. You will also integrate Codex into your pull request workflows, use automated code review, and define team rules on which tasks are delegated and how results are checked before they are merged into the main branch.

Data engineers build and run the pipelines that reporting, analytics and AI applications depend on: ingestion from source systems, transformations in dbt or Spark, orchestration with Airflow or Dagster, and data quality and governance in the warehouse or lakehouse. Much of the work involves SQL, Python, YAML and a great deal of detailed knowledge about schemas and dependencies. AI coding assistants and agents accelerate this work considerably: they write dbt models with tests and documentation, optimise slow queries, create Airflow DAGs, migrate legacy ETL and explain complex lineage. Because data is often sensitive, handling schemas, sample data and credentials responsibly is at the heart of this course.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Data engineers and analytics engineers
  • BI developers responsible for ETL and the data warehouse
  • Platform teams running lakehouse and streaming infrastructure
  • Developers modernising legacy ETL processes

Typical challenges we address

  • Ageing ETL built on stored procedures or proprietary tools with no tests
  • Slow, expensive queries and inefficient Spark jobs
  • Missing data quality checks and unclear ownership of datasets
  • Patchy documentation of data models, lineage and business logic

What you will be able to do

  • You use Codex locally and in the cloud for the development tasks each mode suits best.
  • You write an AGENTS.md that guides the agent reliably through your project.
  • You configure approval modes and sandboxing to match your security requirements.
  • You delegate tasks to the cloud agent and review the results systematically.
  • Use AI agents productively for dbt models, pipelines and SQL optimisation.
  • Modernise legacy ETL with AI support and safeguard results with reconciliation tests.
  • Protect sensitive data by working with schemas, synthetic samples and minimal access rights.

Course outline

Day 1

Module 1 Codex at a glance: CLI, IDE and cloud
  • Comparing how Codex works locally and in the cloud
  • Installing the Codex CLI, signing in and starting it in a repository
  • Interactive sessions, non-interactive execution and model choice
  • Distinguishing tasks that suit a coding agent from those that do not
Module 2 Project knowledge with AGENTS.md
  • Building an AGENTS.md with build, test and lint commands
  • Stating conventions, architectural notes and prohibitions precisely
  • Using nested AGENTS.md files for monorepos and sub-projects
  • Checking effectiveness and refining instructions iteratively
Module 3 Sandboxing, approvals and security
  • Choosing approval modes deliberately, from read-only to largely autonomous
  • Understanding sandbox boundaries for file system and network access
  • Handling secrets, dependencies from the internet and prompt injection
  • Configuration files for team standards and safe defaults
Module 4 Delegating tasks to the cloud agent
  • Configuring cloud environments with setup scripts and dependencies
  • Writing tasks with a clear goal, context and acceptance criteria
  • Launching several tasks in parallel and comparing the results
  • Using logs and test output to understand the proposed solutions

Day 2

Module 5 PR workflows and code review in the team
  • Turning results into pull requests and requesting follow-up changes
  • Using automated Codex code review on GitHub
  • Embedding Codex in scripts and CI pipelines
  • Setting team rules for delegation, review duties and accountability
Module 6 Practice lab: Data Engineering
  • dbt models with tests
  • Optimising SQL queries
  • Airflow and Dagster pipelines
  • Migrating legacy ETL
  • Automating data quality
  • Analysing Spark jobs
Module 7 Data protection, quality & transfer to daily work
  • Data engineering prompts should contain schemas and synthetic sample data rather than real personal records, and database credentials or connection strings must stay out of context. Apply GDPR principles such as purpose limitation and data minimisation, document data flows transparently and check new Python packages and connectors for licensing and supply-chain risk.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

dbt models with tests

The agent writes staging, intermediate and mart models in dbt, adds schema tests, source definitions and descriptions, and runs dbt build iteratively until models and tests pass.

Optimising SQL queries

The AI analyses execution plans, spots expensive joins and missing partitioning or clustering, and rewrites queries so they scan less data and run measurably faster.

Airflow and Dagster pipelines

Requirements become DAGs or assets with a clean dependency structure, retries, sensors, alerting and backfill support, along with tests for the transformation logic. Existing naming conventions are respected.

Migrating legacy ETL

Stored procedures or graphical ETL jobs are analysed, the business logic documented and moved step by step into dbt or PySpark code, safeguarded by reconciliation queries comparing old and new.

Automating data quality

The assistant defines checks for completeness, uniqueness, value ranges and freshness, implements them with dbt tests or Great Expectations and sets up notifications for violations.

Analysing Spark jobs

Spark UI information and logs are interpreted, skew, shuffles and memory issues identified and optimisations such as broadcast joins, repartitioning or caching proposed. Every optimisation is backed by runtime comparisons.

Possible hands-on projects

Data quality dashboard

A small web app that reads results from dbt tests or data quality checks and presents trends, outliers and affected tables at a glance.

Ingestion pipeline for a REST API

A Python pipeline that loads data incrementally from an API, detects schema changes and writes the results as partitioned tables to the lakehouse.

Lineage explorer

A tool that parses the dbt manifest and displays dependencies between sources, models and dashboards as an interactive graph. The impact of changes becomes visible at a glance.

Prerequisites

You have hands-on programming experience, are comfortable with Git and the command line, and know pull-request-based workflows. Confident SQL and Python skills plus practical experience with a data warehouse, dbt, Spark or an orchestration tool.

Technical requirements

Your own laptop with a terminal, Git and a Node.js runtime; a ChatGPT plan with Codex access or an OpenAI API key, plus a GitHub account for the cloud exercises.

Data protection & compliance

Data engineering prompts should contain schemas and synthetic sample data rather than real personal records, and database credentials or connection strings must stay out of context. Apply GDPR principles such as purpose limitation and data minimisation, document data flows transparently and check new Python packages and connectors for licensing and supply-chain risk.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Thu, 3 December 2026 – Fri, 4 December 2026 Munich Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Online Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Stuttgart Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Hanover Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Online Request

Frequently asked questions

When should I use Codex locally and when in the cloud?

Locally you work interactively and close to the code, for example when debugging or making exploratory changes where you want to follow every step. The cloud agent suits well-defined tasks that can run independently, such as adding tests or fixing smaller bugs. You can start several of these in parallel and review each one as a pull request.

What belongs in a good AGENTS.md?

A good AGENTS.md explains how the project is built, tested and checked, which conventions apply and which areas must not be touched. It is concise, concrete and kept up to date. Notes on architecture and common pitfalls also help. During the course you write an AGENTS.md for a project of your own and test how it changes the agent’s behaviour.

How safe is it to let Codex run commands?

By default Codex works inside a sandbox with restricted file and network access and asks for approval before risky steps. You decide how much autonomy to grant through approval modes and configuration. In the course you learn to choose these settings deliberately and to limit risks such as prompt injection or running unfamiliar dependencies.

Does the AI need access to our production data?

No, and it usually makes little sense. For the vast majority of tasks, schemas, metadata, execution plans and synthetic sample data are sufficient. On the course you learn to give the agent exactly this context, restrict read access to development environments and still obtain reliable results.

Will this work with our data warehouse?

The working methods are platform-independent and apply equally to Snowflake, BigQuery, Databricks, Redshift, Synapse or PostgreSQL. What matters is telling the assistant about the SQL dialect and platform specifics such as partitioning or clustering. We show you how to capture this in instruction files.

How much does “OpenAI Codex for Data Engineers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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