Are you a hands-on technical leader ready to shape the future of enterprise data and AI? We are seeking a highly experienced Databricks Architect to lead the design, implementation, and optimization of enterprise-grade data platforms and AI-enabled workloads for a world-renowned global food corporation. If you combine deep Databricks expertise with strong architectural leadership and thrive in a dynamic, managed service delivery model, we want to hear from you! Role Overview & Project Description In this role, you will act as a trusted advisor and technical heavyweight. You will translate complex business and data requirements into scalable, Databricks-based solutions while ensuring robust governance, peak performance, and operational resilience. Architect & Design: Lead the architecture and implementation of Databricks-based data platforms, pipelines, and AI/ML workloads. Define Lakehouse Patterns: Establish scalable patterns using core capabilities like Apache Spark, Delta Lake, Databricks SQL, MLflow , and governance components. Pipeline Engineering: Design robust batch and streaming data pipelines for high-volume, business-critical workloads. Standards & Optimization: Define architecture standards for performance tuning, workload orchestration, reliability, observability, and cost control. Blueprints & Roadmaps: Translate business and technical needs into solution blueprints and execution roadmaps. Cross-Functional Collaboration: Team up with data engineers, analysts, data scientists, DevOps, and business stakeholders to deliver seamless end-to-end solutions. Governance & Security: Ensure rock-solid data governance, security, and compliance across the Databricks environment. AI Foundations: Support advanced analytics by enabling reliable feature engineering, model lifecycle practices, and production-ready data foundations. Technical Leadership: Provide hands-on guidance during delivery, troubleshooting, and optimization of existing workloads. Databricks Expertise: Deep, hands‑on mastery of the Databricks platform (workspace design, cluster strategy, jobs orchestration, and platform optimization). Apache Spark: Strong command of Spark and distributed data processing concepts, including performance tuning. Lakehouse Architecture: Proven experience designing and delivering enterprise data solutions using Delta Lake principles. Languages: Strong proficiency in Python and SQL (Scala is a definite plus!). ETL/ELT Pipelines: Demonstrated capability in building/optimizing large-scale ingestion frameworks and data models. AI/ML Support: Experience supporting workloads via MLflow , managing model lifecycles, and setting up production-ready data. Cloud Platforms: Solid knowledge of cloud-native architecture patterns on AWS, Azure, or GCP (including storage, networking, and identity integration). Data Governance: Experience with access control, data lineage, and compliance frameworks in enterprise environments. DevOps & CI/CD: Familiarity with infrastructure-as-code, monitoring, and operational best practices for data platforms. Problem Solver: Ability to dive into deep technical problem-solving with minimal ramp-up time. #J-18808-Ljbffr