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Specialist

AI for Data and Integration Engineering

Specialist depth for responsible AI use across data discovery, database and contract design, pipelines, transformation, APIs, events, testing, observability, modernization, and governance.

The workplace problem

Why this learning matters

Data, database, API, and integration teams need AI assistance that improves engineering work while preserving correctness, integrity, lineage, privacy, security, compatibility, interoperability, recoverability, and production reliability.

Intended outcomes

What participants will learn to do

  • Apply AI assistance to selected data, database, API, and integration activities with explicit context, lineage, and review.
  • Preserve correctness, integrity, privacy, security, compatibility, interoperability, recoverability, and operational evidence across data stores, pipelines, APIs, and events.
  • Use accountable testing, observability, and incident learning for AI-assisted change.

Application at work

How participants can apply it at work

  • An AI-assisted database, API, data, or integration workflow with lineage and control boundaries.
  • A reviewed schema, pipeline, API, or event-design artifact with verification, compatibility, recovery, and observability evidence appropriate to the selected track.

Shaped to context

Shaped around your context

Flow Cracker shapes this specialist workshop around the customer's data landscape, database and integration architecture, API consumers, approved tools, privacy and security controls, recovery and reliability needs, and governance context.

Entry context: Participants should bring practical experience with data models, databases, pipelines, APIs, events, testing, source control, or production data operations.

Who it is for

Who this is for

  • Data, database, data-platform, integration, API, ETL, and ELT engineers
  • Database administrators, data and integration architects, software engineers, and platform engineers
  • Technical leads and managers responsible for enterprise data movement and interoperability

Opportunity lenses

  • AI to build the product

Capabilities strengthened

  • Engineering, Architecture & Verification
  • Enterprise, Portfolio & Investment Flow

Connected learning

Discuss the context

Shape the learning around the work that needs to change

Share the audience, workplace problem, constraints, and evidence you want the learning to address.

Discuss this learning need