About This Job

Role Purpose

Translate business objectives into clear, testable data engineering stories and acceptance criteria. Own backlog clarity for ETL/ELT changes, DBT models, data quality rules, and reporting outputs so the pod meets data quality SLIs/SLOs (freshness, completeness, accuracy, timeliness).

Key Responsibilities

• Elicit requirements and define acceptance criteria for data pipelines, transformations, and reporting (Tableau/SSRS).
• Author epics/stories aligned to T‑shirt sizing (S/M/L/XL) and service bounds; maintain traceability to business value.
• Define data quality rules and validation checks; ensure SLIs/SLOs are measurable and observable (dashboards, alerts, runbooks).
• Collaborate with Tech Lead on source→target mapping, CDC patterns, and schema‑drift handling; with Scrum Master on sprint predictability.
• Prepare UAT plans and external QA handoff artifacts (test data, lineage, ac...