Lead Architect, Databricks

It's fun to work in a company where people truly BELIEVE in what they are doing! We're committed to bringing passion and customer focus to the business. Lead Architect, Databricks Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a "Cool Vendor" and a "Vendor to Watch" by Gartner. Please visit Fractal | Intelligence for Imagination for more information about Fractal Location : Dallas, Texas (Client onsite)  Note:  This position is not eligible for Immigration Sponsorship currently. Role Overview: Fractal is seeking an experienced Lead Databricks Architect to drive the design, implementation, modernization, and governance of enterprise Data & AI platforms on Databricks for a leading global organization. This is a client-facing architecture role for someone who combines deep technical expertise with strong stakeholder engagement skills. The Lead Databricks Architect will work closely with business stakeholders, product owners, data engineers, data scientists, enterprise architects, and cloud platform teams to build scalable, secure, and business-aligned data platforms and products. The ideal candidate brings strong expertise in Databricks Lakehouse architecture, cloud-native data engineering, analytics, AI/ML enablement, and data governance. They will be responsible for defining solution architecture, establishing engineering standards, guiding delivery teams, resolving complex technical challenges, and serving as a trusted advisor to client stakeholders throughout the solution lifecycle. Success in this role requires a balance of hands-on architecture leadership, technical depth, stakeholder management, and delivery execution, helping organizations accelerate their Data & AI transformation while ensuring solutions are scalable, governed, performant, and aligned to business objectives. Key Responsibilities: Client Partnership & Solution Leadership Serve as the primary Databricks architecture lead for client-facing Data & AI initiatives. Partner closely with business stakeholders, product owners, enterprise architects, engineering teams, and cloud platform teams to deliver business-aligned solutions. Act as a trusted advisor by providing architectural guidance and technical recommendations that balance business requirements, scalability, security, and cost. Translate business and functional requirements into scalable data platform architectures and implementation approaches. Drive architecture decisions and resolve complex technical challenges throughout the delivery lifecycle. Collaborate with client teams to establish engineering standards, solution patterns, and delivery best practices. Provide ongoing technical leadership while remaining actively engaged in execution and delivery. Databricks Architecture & Platform Engineering Design and implement enterprise-scale data platforms leveraging the Databricks Lakehouse Platform. Lead architecture and implementation of: Delta Lake Unity Catalog Databricks Workflows Databricks SQL MLflow Delta Live Tables (DLT) Lakehouse Monitoring Mosaic AI Develop reference architectures, reusable frameworks, and engineering standards to accelerate solution delivery. Design scalable batch, streaming, near real-time, and event-driven processing architectures. Architect enterprise data ingestion, transformation, storage, consumption, and orchestration layers. Design multi-environment deployment strategies supporting development, testing, and production workloads. Drive platform performance optimization, scalability, resilience, and operational excellence. Data Engineering & Data Products Architect reusable data pipelines supporting structured, semi-structured, and unstructured data. Define scalable data product architectures aligned to business domains and enterprise analytics needs. Guide teams on data modeling approaches including Medallion, Dimensional, and Data Vault methodologies. Design integration patterns with enterprise applications, APIs, BI platforms, and downstream analytics ecosystems. Establish standards for data quality, lineage, observability, metadata management, and operational monitoring. Ensure data products are reliable, governed, discoverable, and reusable across the enterprise. Data Governance, Security & Reliability Implement governance frameworks leveraging Unity Catalog and enterprise governance platforms. Define standards for data access controls, security, privacy, lineage, and auditability. Establish enterprise-wide practices for metadata management, data stewardship, and data ownership. Design solutions that meet regulatory, compliance, and enterprise security requirements. Partner with security and infrastructure teams to implement scalable and secure data architectures. Drive platform observability, monitoring, alerting, and support readiness. AI / ML & GenAI Enablement Design enterprise platforms supporting advanced analytics, machine learning, GenAI, and Agentic AI use cases. Enable AI-ready data foundations through strong governance, quality, and data lifecycle management. Architect ML lifecycle capabilities leveraging MLflow and Databricks AI services. Support implementation of Retrieval-Augmented Generation (RAG) architectures using enterprise data. Collaborate with data science teams to operationalize machine learning and AI solutions. Define architecture patterns suppo