Job Description

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.
• Provide technical leadership to engineering teams by reviewing designs, guiding implementation, resolving architectural bottlenecks, and establishing best practices for Databricks-based delivery.
• Evaluate and recommend Databricks capabilities such as Photon, serverless compute, Lakehouse Federation, and streaming patterns to improve scalability, maintainability, and time to value.
• Ensure strong delivery governance through estimation, technical planning, dependency management, risk mitigation, and Agile execution including sprint planning, backlog refinement, and design reviews.

• 10-15 years of experience in data engineering, cloud data platform design, or enterprise data architecture, with at least 5+ years of strong hands-on experience on Databricks and Azure.
• Bachelor’s degree in computer science, Information Technology, Engineering, or a related discipline; master’s degree is preferred.
• Strong expertise in designing modern data architectures, including Lakehouse, medallion architecture, data modeling, data warehousing, and scalable ingestion and transformation frameworks.
• Deep technical proficiency in Databricks, PySpark, Python, SQL, Delta Lake, Databricks Workflows, Auto Loader, and Delta Live Tables.
• Strong experience with Azure cloud services such as Azure Data Factory, Azure Data Lake Storage, Azure Key Vault, Azure DevOps, and integration of Databricks with broader enterprise cloud ecosystems.
• Hands-on experience in defining architecture standards, reusable design patterns, CI/CD strategy, environment management, and delivery best practices for enterprise-scale data platforms.
• Experience with data governance, lineage, and security frameworks including Unity Catalog, role-based access controls, and integration with enterprise governance tools such as Azure Purview.
• Proven ability to optimize large-scale Spark and Databricks workloads, including performance tuning, cluster sizing, workload management, and cost optimization.
• Experience engaging with business stakeholders, product owners, architects, and engineering teams to convert business requirements into scalable solution designs and implementation roadmaps.
• Strong communication, leadership, and problem-solving skills with the ability to mentor teams, review technical designs, and drive architecture decisions across cross-functional programs.
• Preferred: Knowledge of insurance domain data models, regulatory considerations, and analytics use cases relevant to underwriting, claims, pricing, or risk functions.