create-stories

Automate sprint backlog generation by decomposing approved low-level design documents, requirement specifications, and data models into structured epics and stories. This process ensures full traceability to source artifacts and aligns with team capacity inputs for data engineering pipelines.

Story decomposition for medallion-layer data pipelines breaks technical designs into traceable sprint tasks. It enforces mandatory validation rules including layer closure sequences, Spark Expectations end-to-end coverage, and phased contract handling to eliminate common backlog errors.

Yes, generating a data engineering sprint backlog requires approved upstream artifacts. The process consumes low-level design documents, data models, and requirement specifications like DRD, HLD, DMS, STM, and DQS to ensure proper dependency mapping and upstream approval gates.

Missing inputs like team capacity and sprint length are handled through interactive decision gathering. Structured Q&A collects missing decomposition inputs, priority schemes, and story granularity from the user before generating output to ensure the backlog aligns with team workflows.

Mandatory guardrails enforced during sprint backlog creation include Spark Expectations end-to-end coverage, phased contract handling, and layer closure sequences for performance optimization, integration testing, and deploy validation to produce production-ready backlogs.

Manual story decomposition delays data engineering sprints because it takes hours to map requirements and data models into stories, often missing critical traceability, dependency mapping, and compliance with team story standards, which leads to rework.