Solution Architect

Define and implement end-to-end data platform architecture utilizing Databricks, Apache Spark, Delta Lake, Unity Catalog, and Azure Data Services. Design scalable, secure, and resilient Lakehouse architectures that support enterprise reporting, advanced analytics, machine learning, and data products. Establish architectural standards and best practices for data ingestion, transformation, storage, consumption, and lifecycle management. Design multi-tenant data platform solutions leveraging Unity Catalog while enforcing governance, security, metadata management, and data-sharing principles. Define logical and physical data models, integration patterns, and framework accelerators to improve platform consistency and reuse. Lead architecture reviews and ensure alignment with enterprise architecture, security, and compliance standards. Technical Leadership & Delivery Governance Serve as the primary technical authority for the data platform program and guide engineering teams through design and implementation decisions. Provide technical leadership to onshore and offshore teams, ensuring solution quality, consistency, and adherence to architectural standards. Review solution designs, code, deployment strategies, and performance optimization approaches. Establish engineering best practices for scalability, observability, reliability, and operational support. Drive issue resolution for complex technical challenges and act as an escalation point for critical platform concerns. Collaborate with business stakeholders, product owners, data engineers, data scientists, and enterprise architects to translate business requirements into technical solutions. Evaluate emerging Databricks capabilities, Azure services, and industry best practices to enhance platform maturity and reduce technical debt. Drive automation across deployment, testing, monitoring, and operational processes using Azure DevOps and modern CI/CD frameworks. Establish reusable frameworks, accelerators, templates, and operational playbooks to improve delivery velocity and platform consistency. Define and monitor platform KPIs related to performance, cost optimization, data quality, reliability, and operational efficiency. Collaborate with governance and security teams to implement data lineage, access controls, auditing, and compliance frameworks. Required Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, or related field. 10–15+ years of experience in Data Engineering, Data Architecture, or Cloud Data Platform leadership roles. Hands-on expertise with Databricks, Apache Spark, Delta Lake, and Azure Data Services. Strong understanding of Lakehouse architecture, modern data warehousing principles, and enterprise-scale data platforms. Proven experience designing and implementing batch and real-time ETL/ELT data pipelines. Strong proficiency in SQL, data modeling, schema design, and performance tuning. Experience with structured and semi-structured data formats including Parquet, ORC, Avro, and JSON. Deep understanding of cloud security, identity management, data governance, and metadata management using Unity Catalog. Experience implementing CI/CD pipelines and infrastructure automation for data platforms. Demonstrated experience leading distributed engineering teams and managing large-scale platform implementations. Excellent communication, stakeholder management, analytical, and problem-solving skills. Preferred Qualifications Experience implementing data governance, data lineage, metadata management, or data quality frameworks. Exposure to domain-driven design, data product operating models, and data mesh concepts. Experience supporting AI/ML, GenAI, or advanced analytics workloads on Databricks. Databricks, Azure, or cloud architecture certifications. Experience within the Property & Casualty Insurance domain or financial services industry. Familiarity with FinOps, cloud cost optimization, and platform observability frameworks. #J-18808-Ljbffr Innorev Technologies Inc