Lead Data Engineer (10+ years with MS Fabric, Azure, ADF, Data Platforms only)

Job Summary We are looking for a Lead Data Engineer to help shape and grow our next-generation data engineering capability. This is a hands-on technical leadership role for an experienced engineer who can set technical direction, establish engineering standards, mentor and elevate other engineers, and build enterprise-scale data solutions. You will help design and evolve a modern, scalable, cloud-based data platform that may span technologies such as Microsoft Fabric, Databricks, Snowflake, and cloud-native services. Rather than being tied to a single technology stack, we are looking for someone who understands modern data architecture principles and can make thoughtful technology choices based on business, data, scalability, interoperability, and operational needs. Just as importantly, we are looking for an AI-first engineering leader who can help the team rethink how we design, build, test, operate, and continuously improve data products using AI. Key Responsibilities Lead Engineering Excellence Provide hands-on technical leadership for a growing team of data engineers. Set engineering standards and reusable patterns across architecture, coding, testing, data modeling, documentation, observability, security, and deployment. Lead architecture discussions, technical design reviews, and code reviews. Mentor engineers and develop technical depth, ownership, problem-solving, and engineering judgment across the team. Translate complex initiatives into clear technical approaches and guide teams from design through production. Foster a culture of ownership, collaboration, curiosity, experimentation, continuous learning, and accountability. Build & Evolve a Modern Data Platform Lead the architecture and engineering of scalable, secure, and governed enterprise data platforms. Design solutions across lakehouse, data warehouse, data lake, streaming, and data-product architectures based on the needs of the use case. Build batch, incremental, near-real-time, and event-driven data processing solutions. Create reusable engineering frameworks for ingestion, transformation, orchestration, data quality, metadata, and data delivery. Design scalable data models supporting operational, analytical, reporting, and AI use cases. Establish patterns that promote interoperability and reduce unnecessary platform and vendor dependencies. Evaluate technology choices based on performance, scalability, reliability, security, cost, maintainability, and business value. Remain hands-on and contribute directly to engineering solutions when needed. Champion AI-First Engineering Champion the use of AI across the engineering lifecycle, including requirements, architecture, development, testing, documentation, troubleshooting, and operations. Establish responsible practices for reviewing, validating, securing, and governing AI-generated engineering artifacts. Promote spec-driven development, using well-defined requirements, data contracts, acceptance criteria, and automated tests.