Website:
galent.com
Job details:
Role Summary:
The Data Architect owns data modeling, schema governance, multi-tenant data routing and isolation, and the ETL migration toolkit for ~250 customer SQL Server databases. The role designs both the PostgreSQL 16 platform database (workflow state, form schemas, audit logs, report configurations) and the SQL Server customer-database schemas, manages schema consistency across the installed base, and architects the reporting and future SQL Server → PostgreSQL migration path.
Key Responsibilities
- Design the platform PostgreSQL 16 schema: workflow state, form-schema JSON storage (JSON Schema draft-07), report configurations, audit logs
- •Govern the customer SQL Server schema (200–400+ tables per tenant across ~250 customers) including schema-drift detection and the golden schema manifest
- Design the multi-tenant data-routing model: Hibernate Filters for tenant_id injection, request-scoped TenantContext propagation, and DB-level enforcement guarantees
- Architect the SIMS Migration Toolkit: schema validator, chunked JDBC data extractor with checksums, reconciliation validator (row counts, FK integrity, spot-check sampling), rollback manager with atomic snapshot and restore-on-failure, dry-run pipeline
- Design the 4-layer Reporting Engine data architecture (Typed Data Access API → Report Definition Engine → React Presentation Layer → future AI Query Layer)
- Drive schema-consistency analysis across all customer databases before any production migration script is written
- Define the future SQL Server → PostgreSQL migration architecture so that portability is designed in from day one (zero rework when the migration is triggered)
- Establish encryption-at-rest, TLS 1.3 encryption-in-transit, and AWS Secrets Manager integration for database credentials
- Apply AI-augmented data-engineering practices: AI-driven schema-drift detection across the installed base, AI-generated ETL reconciliation reports, AI-assisted query generation and performance-tuning recommendations, and AI-authored migration runbooks
- Author data-classification schemas aligned with NISPOM, SAP, and SCI handling requirements
Required Qualifications:
- 9+ years data-architecture experience with both PostgreSQL and SQL Server at enterprise scale
- Deep expertise in multi-tenant data-isolation patterns (row-level security, Hibernate Filters, tenant-per-schema, tenant-per-database)
- Proven ETL design experience for 100+ customer migrations including validation, reconciliation, and rollback
- Expert JPA / Hibernate, including Hibernate Filters, custom tenant resolvers, and auditing frameworks
- Strong SQL performance tuning on both PostgreSQL 16 and SQL Server 2019+
- Experience designing JSON Schema storage and per-tenant versioning strategies in relational databases
- Familiarity with audit-trail architectures for compliance-regulated enterprise customers
- Apply AI-augmented data engineering practices: AI-driven schema-drift detection, LLM-assisted SQL generation and performance tuning, AI-generated ETL reconciliation reports, and AI-authored migration runbooks
- Deliver AI-ready data architecture: semantic field naming, strongly-typed schemas, and metadata contracts consumable by future AI agents without transformation
- Demonstrated proficiency with AI-assisted development tools (Cursor, Claude Code, GitHub Copilot, or equivalent) for schema design and query optimization
- Experience with vector stores, embeddings, or RAG-ready data modeling patterns.
Preferred Qualifications
- AWS Certified Database or Solutions Architect Professional
- Experience with AWS RDS, AWS DMS, or similar managed-database migration services
- PostgreSQL 16 advanced features (logical replication, partitioning, JSONB indexing)
- Background in federal defense / classified data-handling environments
- Experience bundling PostgreSQL for air-gapped deployment (Docker Compose, embedded distributions)
Technical Skills
- Databases: PostgreSQL 16, SQL Server 2019+, AWS RDS
- ORM: Hibernate 6.x, JPA 3.x, Flyway, Liquibase
- ETL: Custom Java ETL, Spring Batch, Debezium (optional)
- Schema tooling: Liquibase, SchemaSpy, dbt (for reporting)
- Languages: Java 21, SQL (T-SQL + PL/pgSQL), Python (ETL scripts)
- AI-augmented data engineering: AI schema-drift detection, LLM-assisted SQL generation and tuning, AI-generated ETL reconciliation reports, AI-authored migration runbooks
- Data modeling: ERD, dimensional modeling, EAV for configurable forms
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