7 scopes · ranked by best match
Implement automated checks, thresholds, alerts, lineage signals, and issue visibility for critical datasets. Finish state: data problems found by monitors in hours, not by executives in meetings.
VIEWS 30D 0Unify customer identities and interactions from multiple systems into a coherent customer-level data model. Finish state: one queryable customer record per real customer, with identity resolution you can audit.
VIEWS 30D 0Implement event ingestion and streaming transformations for low-latency analytics and operational use cases. Finish state: defined event streams flowing to consumers in seconds, with lag monitored.
VIEWS 30D 0Centralize structured and unstructured data in a governed lake for analytics, ML, and downstream processing. Finish state: a governed lake teams can actually find things in — not a data swamp.
VIEWS 30D 0Create a structured cloud warehouse with a tested transformation layer and modeled datasets ready for analytics and BI. Finish state: governed, documented datasets your analysts actually query.
VIEWS 30D 0Ongoing data engineering and analytics capacity working a metrics backlog: pipeline build and operations, self-service BI, AI-powered data quality, and ML-ready infrastructure — delivered as a standing pod.
VIEWS 30D 0A working ELT baseline for your business: source ingestion, a governed warehouse, and your first dashboards — built on dbt, Airflow, and your cloud, with data quality checks from the first pipeline.
VIEWS 30D 0