Solution Architect – (Azure Databricks)

Solution Architect – (Azure Databricks)We are seeking a Solution Architect to design and drive enterprise-scale data engineering architectures on Azure Databricks and cloud platforms. This role will define end-to-end data solutions, architecture patterns, and engineering standards to support analytics, AI/ML, and operational use cases.The ideal candidate will combine deep technical expertise with strong business alignment, enabling scalable, secure, and high-performing data platforms.Solution architecture is critical to ensuring standardized, scalable, and governed data ecosystems across domains and use cases.Key Responsibilities1. Architecture & Solution DesignDefine and implement end-to-end data architecture (ingestion ? transformation ? consumption)Design scalable Lakehouse architectures (Databricks, Delta Lake)Develop reference architectures, reusable frameworks, and design patternsAlign architecture with performance, scalability, security, and cost optimization goals2. Data Modeling & Domain ArchitectureDefine canonical data models, dimensional schemas, and semantic layersArchitect enterprise data across domains: Claims, Member, Provider, EligibilityEnsure data consistency, interoperability, and reuse across teams3. Azure Databricks & Cloud EnablementArchitect solutions using:Azure Databricks (Mandatory)Azure Data Services (ADF, ADLS, Synapse)Guide engineering teams on:Spark optimization, Delta Lake design, and data pipeline best practicesEnable multi-cloud integrations when required4. Governance, Security & ComplianceDefine and enforce:Data governance frameworks (lineage, quality, metadata)Access control, RBAC, and data security policiesEnsure compliance with:HIPAA, HITRUST, and enterprise regulatory standardsEstablish data quality SLAs and auditability mechanisms5. Engineering & Platform AlignmentWork closely with:Data Engineering teamsPlatform Engineering teamsAI/ML Engineering teamsProvide architectural oversight for pipelines, workflows, and platform capabilitiesDrive standardization across CI/CD, DataOps, and MLOps practices