Databricks Architect

Avanade Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia

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Avanade Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia

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Summary

Our talented Data & AI Practice is made up of globally recognized experts - and there’s room for more analytical and ambitious data professionals. If you’re passionate about helping clients make better data-driven decisions to tackle their most complex business issues, let’s talk. Take your skills to a new level and launch a career where you can truly do what matters.

Key Responsibilities

• Architectural Leadership : Define and own the end-to-end technical architecture and roadmap for the enterprise data platform on Databricks, including data ingestion, transformation, storage, and consumption layers.
• Lakehouse Design : Design and champion the implementation of the Lakehouse architecture utilizing Delta Lake, Databricks Unity Catalog, and Databricks SQL Warehouse to support all data, analytics, and AI / ML initiatives.
• Data Governance & Security : Architect and enforce enterprise-level data governance, security, and access control policies using Unity Catalog (e.g., fine-grained access, lineage tracking, auditing).
• Technical Guidance & Mentorship : Provide technical leadership, guidance, and mentorship to a team of Databricks Engineers. Conduct architectural reviews, code audits, and ensure adherence to best practices and standards.
• Performance and Cost Optimization : Define strategies for, and lead, major performance tuning and cost optimization initiatives across all Databricks workloads, clusters, and Delta Lake storage.
• Cloud Integration : Lead the integration of Databricks with core cloud services (AWS, Azure, or GCP) and other enterprise systems (e.g., data catalogs, BI tools, ML platforms).
• Stakeholder Collaboration : Engage with executive stakeholders, data scientists, and business leaders to understand complex requirements and translate them into robust, scalable technical designs and solution blueprints.
• DataOps and Automation Strategy : Define the DevOps / DataOps strategy for the Databricks environment, including continuous integration / continuous delivery (CI / CD) pipelines and Infrastructure-as-Code (IaC) using tools like Terraform or Databricks Asset Bundles.
• Innovation : Evaluate new Databricks features, open-source technologies (e.g., MLflow), and industry trends to drive continuous platform improvement and competitive advantage.

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