Training course · Agentic Coding · Data Engineering

Agentic Coding for Data Engineers

Use autonomous coding agents effectively: specs instead of prompts, task decomposition, test-driven loops, multi-agent setups and governance.

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

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

Agentic coding means working with AI agents that carry out development tasks largely on their own: they read code, plan changes, run commands and tests, and correct themselves based on the results. Tools such as Claude Code, OpenAI Codex, the GitHub Copilot coding agent and Cursor’s agent mode have made this way of working practical for everyday use. This vendor-neutral course teaches the underlying methodology so that you achieve reliable results whatever the tool, and redefine the developer’s role within your engineering team.

The core shift is from ad hoc prompts to robust specifications. You will learn to decompose and describe tasks so that agents solve them reliably, to set up test-driven loops in which tests act as objective acceptance criteria, and to run several agents in parallel or with a division of labour. We also cover review strategies for agent-generated code and team governance: permissions, security boundaries, accountability, cost control and the gradual introduction of agentic workflows into your existing development processes.

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 understand how coding agents work and choose the right tool and level of autonomy.
  • You write specifications and project context that agents can work with reliably.
  • You break initiatives into verifiable sub-tasks and steer agents through test-driven loops.
  • You use multi-agent setups and parallel work in a controlled way.
  • 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 Foundations of agentic development
  • How coding agents work: tool calls, context windows and feedback loops
  • Comparing tools: terminal agents, IDE agents and cloud agents
  • Recognising suitable tasks and choosing levels of autonomy deliberately
  • The new role in the team: from implementation to steering and acceptance
Module 2 Specifications instead of prompts
  • Describing tasks with goal, context, constraints and acceptance criteria
  • Keeping project context in files such as CLAUDE.md, AGENTS.md or rules
  • Plan-first approach: let the agent draft a solution and review it before implementation
  • Versioning specifications and maintaining them as part of the repository
Module 3 Task decomposition and test-driven agent loops
  • Breaking large initiatives into independent, verifiable sub-tasks
  • Having tests written first and using them as an objective stopping criterion
  • Using linters, type checks and builds as automatic feedback
  • Recognising stuck loops and restarting with a new strategy
Module 4 Multi-agent setups and parallel work
  • Defining specialised subagents for research, implementation, testing and review
  • Organising parallel work with branches, worktrees and isolated environments
  • Orchestration: merging results and resolving conflicts
  • Sharing tools and data sources through MCP

Day 2

Module 5 Review, security and team governance
  • Review strategies for large, agent-generated changes
  • Defining permissions, sandboxing and protection against prompt injection
  • Embedding agents in CI pipelines and pull request processes
  • Planning team guidelines, accountability, costs and a phased rollout
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 several years of development experience, are confident with Git, testing and CI, and ideally have already used AI coding tools. Confident SQL and Python skills plus practical experience with a data warehouse, dbt, Spark or an orchestration tool.

Technical requirements

Your own laptop with Git, a terminal and your development environment, plus access to at least one coding agent such as Claude Code, Codex, Copilot or Cursor.

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.

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Tue, 1 December 2026 – Wed, 2 December 2026 Kaiserslautern Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Online Request
  • Thu, 17 December 2026 – Fri, 18 December 2026 Bremen Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Salzburg Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Online Request

Frequently asked questions

How does agentic coding differ from vibe coding?

Vibe coding relies on fast, intuitive iteration in natural language and is particularly suited to prototypes. Agentic coding is an engineering discipline for professional codebases: precise specifications, tests as acceptance criteria, clear permissions and structured reviews ensure that agents deliver reliable, maintainable results even in complex, long-lived projects.

Is the course tied to a specific tool?

No. The course teaches methods that work with Claude Code, OpenAI Codex, the GitHub Copilot coding agent, Cursor and comparable tools. The exercises highlight differences and common ground, for example in context files, permissions and subagents. You can work with whichever tool your organisation already uses or plans to adopt.

How do we stay in control of quality and security with autonomous agents?

Control comes from several layers: tightly scoped permissions and sandboxing, tests and static analysis as automatic checks, mandatory human review before merging, and clear team rules on accountability. During the course you develop a governance concept that fits your processes, security requirements and regulatory obligations.

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 “Agentic Coding 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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