Self-service data-quality platform
Designed a self-service platform for defining, running, monitoring, and investigating data-quality checks across distributed data workloads.
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Designed a self-service platform for defining, running, monitoring, and investigating data-quality checks across distributed data workloads.
Built a Python validation library that turns sample events into editable contracts with reusable field-level quality rules.
Led development of a configurable data-quality platform with reusable connectors for profiling, validation, and reconciliation across varied sources.
Dashboards usually reveal a data problem after it has travelled through half the platform. Data contracts move the conversation upstream: clear schemas, ownership, compatibility rules, and a better way to change pipelines.
Architectural pattern for a multi-tenant data-quality platform across many teams. Why centralising contracts beats centralising data, and how the SDK + control plane + fact/dim store pattern works regardless of which tools you reach for.