Training course · Claude Code · Data Engineering

Claude Code for Data Engineers

Agentic coding with Claude Code in the terminal and IDE: CLAUDE.md, subagents, hooks, MCP, CI pipelines and secure permissions.

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

“Claude Code 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 Claude Code productively, safely and transparently in Data Engineering – from the fundamentals to advanced workflows.

Claude Code is Anthropic’s agentic coding assistant, working directly in your terminal and in popular IDEs. Rather than completing individual lines, it reads your repository, plans changes across multiple files, runs commands, executes tests and prepares commits. Over two days you will learn to use it with deliberate control: clear project instructions in CLAUDE.md, careful context management, and a workflow in which you set the direction as a developer and can trace every change the agent makes back to a clear intention.

The course is built around real engineering work: diagnosing bugs, delivering features, refactoring legacy code and filling gaps in test coverage. You will configure subagents for specialised tasks, enforce quality checks with hooks, connect external systems through the Model Context Protocol (MCP) and capture team knowledge as reusable skills. Finally, you will run Claude Code headless in CI pipelines and define permissions, sandboxing and approval rules so that its use remains defensible even in security-sensitive environments.

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 Claude Code for multi-step development tasks while keeping full control over every change.
  • You maintain a CLAUDE.md that effectively conveys project conventions, commands and architectural guidance to the agent.
  • You extend Claude Code with subagents, hooks, MCP servers and skills tailored to your team.
  • You integrate Claude Code into Git workflows and CI pipelines with traceable, test-backed results.
  • 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 How Claude Code works
  • Installation, sign-in and use in the terminal, VS Code and JetBrains IDEs
  • Understanding the agentic loop: read, plan, act, verify
  • Using plan mode to agree on an approach before any code changes
  • Writing effective task briefs with goal, context and acceptance criteria
Module 2 Project context with CLAUDE.md and context management
  • Structuring CLAUDE.md at project, directory and user level
  • Documenting build, test and style conventions so the agent follows them reliably
  • Managing the context window with targeted file references, compaction and fresh sessions
  • Resuming sessions and breaking long tasks into manageable stages
Module 3 Extending Claude Code with subagents, hooks, MCP and skills
  • Defining subagents for code review, research or test generation
  • Using hooks to enforce formatting, linting or checks before and after tool calls
  • Connecting MCP servers for issue trackers, databases or internal documentation
  • Creating custom slash commands and skills for recurring team workflows
Module 4 Git workflows, testing and quality assurance
  • Creating branches, meaningful commits and pull requests with Claude Code
  • Test-first work: have tests written before the implementation
  • Reviewing diffs systematically and rejecting or refining changes
  • Organising parallel work with Git worktrees and multiple sessions

Day 2

Module 5 Headless operation, CI and security
  • Running Claude Code non-interactively in scripts and CI pipelines
  • Automated reviews and issue handling in GitHub or GitLab workflows
  • Configuring permission modes, allow and deny rules, and sandboxing
  • Handling secrets, prompt injection risks and organisation-wide policies
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 in at least one language and are comfortable with Git and the command line. Confident SQL and Python skills plus practical experience with a data warehouse, dbt, Spark or an orchestration tool.

Technical requirements

Your own laptop with admin rights, Git, a Node.js runtime and an up-to-date editor; a Claude subscription or Anthropic API access is required for the 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.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Stuttgart Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Berlin Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Online Request
  • Mon, 8 February 2027 – Tue, 9 February 2027 Munich Request
  • Thu, 25 February 2027 – Fri, 26 February 2027 Online Request

Frequently asked questions

How is Claude Code different from traditional code completion?

Code completion suggests the next few lines, whereas Claude Code acts as an agent that handles whole tasks: it searches the repository, plans changes across several files, runs tests and commands, and corrects itself based on the results. You define the goal and review the outcome instead of steering every single line yourself.

Is Claude Code secure enough for enterprise use?

By default, Claude Code asks for approval before editing files or running commands. Permission rules, sandboxing, managed settings and hooks let you define precisely what the agent may do. During the course you learn to configure these mechanisms to match your security and data protection requirements and to assess risks such as prompt injection.

Does Claude Code work with my language and tech stack?

Yes. Claude Code is language-agnostic because it works with files, terminal commands and your existing build and test tooling. Whether you use Python, Java, TypeScript, Go, SAP-related code or infrastructure as code, what matters most is a well-written CLAUDE.md describing your commands and conventions. Each course variant adds the relevant domain focus.

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 “Claude Code 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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