Vacancy Description
The problem
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Organizations with multiple locations run different systems (EHR, HR, finance). Each dumps raw data in different formats and schemas. We want to unify this into a central semantic model – without forcing locations to change their systems.
What we’re trying to achieve
Automatically understand what’s in a raw database schema
Detect when tables across different sources represent the same entity
Merge and map local data to a central ontology
Make this process automated with human‑in‑the‑loop supervision
One route we’re exploring
Cube‑based semantic layer(s) with agent‑assisted schema generation and entity matching. But we’re open to other approaches: dbt, custom tooling, graph‑based solutions, etc.
Your profile
5+ years data engineering
Deep experience with data modeling and sche...
Lea atentamente toda la información sobre esta oportunidad y luego utilice el botón de solicitud de abajo para enviar su CV y su candidatura.
Organizations with multiple locations run different systems (EHR, HR, finance). Each dumps raw data in different formats and schemas. We want to unify this into a central semantic model – without forcing locations to change their systems.
What we’re trying to achieve
Automatically understand what’s in a raw database schema
Detect when tables across different sources represent the same entity
Merge and map local data to a central ontology
Make this process automated with human‑in‑the‑loop supervision
One route we’re exploring
Cube‑based semantic layer(s) with agent‑assisted schema generation and entity matching. But we’re open to other approaches: dbt, custom tooling, graph‑based solutions, etc.
Your profile
5+ years data engineering
Deep experience with data modeling and sche...
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