Solution Architect

We are looking for an accomplished Databricks Solution Architect / Data Architect to lead the design, modernization, and implementation of enterprise-scale data platforms on Azure. In this senior role, you will define target-state data architecture, establish scalable Lakehouse design patterns, and guide the development of high-performance data pipelines using Databricks, Delta Lake, and related cloud-native services. You will play a pivotal role in shaping architecture standards across data ingestion, transformation, modeling, governance, security, and consumption layers, while ensuring platform scalability, reliability, performance, and cost efficiency. This role will work closely with business stakeholders, product teams, and engineering squads to translate complex business requirements into robust technical solutions that enable advanced analytics, AI/ML, and data-driven decision-making across insurance-focused business domains.

• Lead the end-to-end architecture and solution design for enterprise data platforms on Azure, with strong focus on Databricks Lakehouse, Delta Lake, and scalable cloud-native data ecosystems.

• Define target-state data architecture, ingestion patterns, transformation frameworks, and serving layers to support reporting, advanced analytics, ML, and business-critical decisioning use cases.

• Design and implement robust ETL/ELT pipelines using PySpark, SQL, Databricks Workflows, Auto Loader, and Delta Live Tables for batch and near real-time processing.

• Own architecture standards for data modeling, medallion design, reusable engineering patterns, CI/CD, code quality, environment strategy, and release management across Databricks solutions.

• Drive platform governance and security using Unity Catalog, RBAC/ABAC controls, lineage, auditability, and integration with enterprise governance services such as Purview.

• Optimize solution performance by tuning Spark workloads, cluster policies, partitioning strategy, file sizing, caching, and compute cost management for large-scale data processing.

• Collaborate with business stakeholders, product owners, analysts, architects, and downstream consumers to translate functional and non-functional requirements into scalable technical designs.