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Founding Data Architecture for a Preventive Health Ecosystem

Lead Data Architect
Data Architecture Privacy-by-Design PostgreSQL HealthTech Data Governance
Stack: PostgreSQL, Python, SQL, ETL Pipelines

Context

SaliHub is building a preventive health ecosystem that transforms daily health signals into longitudinal user insights and real-time dashboards. The platform handles sensitive health data — including PII and PHI — and must comply with LOPD privacy regulations from day one.

As the first data hire, my role was to design the entire data foundation from scratch: the data model, the governance framework, and the infrastructure to support a scalable, multi-tenant architecture.

Approach

Data Model Design

Architected the founding data model and core database schema, translating complex LOPD privacy requirements and business logic into a secure, multi-tenant architecture. The schema was designed to support longitudinal health tracking while maintaining strict tenant isolation.

Privacy & Governance Framework

Established Privacy-by-Design as a core principle and defined the organization’s data governance framework:

  • Strict access controls with role-based permissions
  • Audit trails for all data access and modifications
  • Data classification and handling policies for PII/PHI
  • Consent management integrated into the data layer

Data Infrastructure

Building scalable ETL pipelines using Python and SQL to transform raw health signals into structured, queryable insights. The pipeline architecture supports both batch processing for historical analysis and near-real-time updates for dashboards.

Cross-Functional Collaboration

Partnering with Product and Engineering to translate business goals into measurable data capabilities — ensuring the data architecture supports both current product needs and future analytics requirements.

Technical Details

  • Database: PostgreSQL with multi-tenant schema design
  • Privacy: LOPD compliance, PII/PHI handling, audit trails, access control
  • Pipelines: Python and SQL-based ETL for health data transformation
  • Outputs: Longitudinal user insights, real-time dashboards

Status

This is an ongoing project. The founding data model and governance framework are in place, and I am actively building out the pipeline infrastructure and analytics capabilities.