Senior / Lead Platform Architect

Senior / Lead Platform Architect

Remote

Long Term

Platform Architecture Leadership:

·       Define and maintain the Azure Databricks reference architecture for AI data preparation, grounding (RAG), orchestration, telemetry, and governance.

·       Establish Databricks platform standards and guardrails, including workspace patterns, Unity Catalog design, compute policies, and cost controls.

·       Ensure Unity Catalog is the system of record for AI data access, enforcing fine grained permissions, data masking, lineage, and auditability.

·       Standardize embedding, feature, context, and underlying data model design to enable reuse of AI-ready data assets across use cases and business domains.

·       Partner with business, analytics, and engineering teams to translate business capabilities and processes into scalable data models.

·       Architect secure integration patterns between Databricks and downstream AI services or applications, preventing unapproved data egress.

·       Embed quality engineering into AI pipelines using MLflow, evaluation datasets, telemetry, and drift monitoring before production rollout.

·       Ensure production readiness and operability of Databricks AI workloads through Jobs/Workflows standards, monitoring, and KTLO handoff.

·       Apply AI security and compliance by design within Databricks, including identity enforcement, sensitive data protection, and audit logging.

·       Govern data model lifecycle management, including metadata standards, lineage, schema evolution, versioning, and model review processes.

·       Ensure data models support AI/ML, analytics, and operational use cases while preserving consistency, traceability, and regulatory compliance.

Required Qualifications

·       10+ years in platform, solution, or enterprise architecture, including significant hands-on experience in enterprise data architecture and data modeling within Azure-based environments such as Azure Databricks.

·       Proven experience designing AI/analytics data platforms, including governance, security, large-scale data access patterns, and data model alignment across multiple domains and source systems.

·       Strong understanding of RAG, vector retrieval, data governance, metadata management, lineage, and observability in production environments.

·       Experience working in regulated environments with security, privacy, compliance, and data stewardship/model governance requirements.

 Preferred Qualifications

·       Experience establishing or governing enterprise information models, business glossaries, and semantic layers for analytics and AI.

·       Familiarity with data modeling tools and modeling methodologies such as normalized modeling, dimensional modeling, Data Vault, or domain-driven design.

·       Familiarity with Unity Catalog, MLflow, Vector Search, and Azure-native security patterns.

·       Experience integrating enterprise data models with metadata, catalog, lineage, and governance tooling.

·       Azure certifications (AZ 305, AI 102, AZ 500) strongly preferred.