Role Description We are seeking an accomplished, technology-driven Lead Data Platform Architect / Migration Specialist to spearhead the modernization of our core enterprise financial and tax allocation engines. In this role, you will lead the architectural design, definition of migration strategies, and hands-on implementation to transition large-scale legacy relational database systems (SQL Server/T-SQL) into a modern, cloud-native Databricks Lakehouse platform. The ideal candidate will have extensive experience in high-throughput distributed systems, Databricks compute optimization, performance tuning, and complex pipeline orchestration. Key Responsibilities - Architecture & Strategy: Validate, refine, and own the target architecture on Databricks. Define robust migration strategies and production-ready reference patterns to convert 150+ complex stored procedures into PySpark and Structured/Declarative Pipelines (SDP). - Pipeline Engineering: Design distributed processing frameworks, control flows, and configuration-driven parameter handling for both full and incremental recalculation modes. - Performance Optimization: Address performance deltas between small and large workloads. Architect and implement acceleration techniques such as caching, partition pruning, cluster sizing, and offline/pre-calculation strategies to maintain sub-30-second user-facing reporting SLAs. - Orchestration & Observability: Design and deploy enterprise-level pipeline orchestration using tools like Apache Airflow or Databricks Workflows. Integrate robust logging, error handling, and observability patterns into existing enterprise monitoring frameworks. - Governance & Security: Implement data governance models, data lineage, and schema evolution utilizing tools like Unity Catalog. - AI-Assisted Delivery & Code Quality: Establish best practices for AI-assisted code generation (e.g., using Claude or advanced LLMs), providing code-review patterns and refactoring frameworks to ensure maintainable and performant output. - Team Enablement: Lead code walkthroughs, design reviews, and pair-programming sessions with the development team to accelerate knowledge transfer and technical excellence. Qualifications - Deep expert-level knowledge of Databricks (Lakehouse architecture, Delta Lake, Unity Catalog) and Apache Spark / PySpark. - Strong background in relational databases, with advanced proficiency in SQL Server, T-SQL, and Stored Procedures. - Ability to reverse-engineer and refactor legacy database logic into distributed paradigms. - Hands-on experience with Apache Airflow or similar modern workflow orchestrators. - Proven track record in cost optimization (FinOps), cluster tuning, autoscaling configurations, and handling skewed data profiles. - Experience with Infrastructure as Code (Terraform), data build tool (dbt), testing frameworks (PyTest), and automated Git-based workflows. - 10+ years of experience in Data Engineering/Architecture, with at least 3+ years specifically leading large-scale cloud data migrations. - Bachelor’s or Master's degree in Computer Science, Engineering, or a related technical field. Preferred Certifications - Databricks Certified Data Engineer Associate / Professional - Databricks Certified Solutions Architect - AWS Certified Database Specialist or equivalent Cloud Certifications