WIN_Data_Architect

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Overview:

The Data Architect defines and governs the end-to-end data and analytics architecture, designing scalable, secure, and future-ready ecosystems across OCI, Snowflake, and BI platforms. The role enables advanced analytics and ML by establishing medallion architecture, semantic layers, and ML Ops-ready data foundations aligned to business strategy.

Responsibilities:

Enterprise Architecture & Roadmap Alignment

Develop and maintain architecture blueprints aligned with initiatives such as ML Data Warehouse, Semantic Layer, and Medallion Architecture data warehousing.
Translate roadmap priorities into actionable architecture designs that support model deployment, governance, and advanced analytics.
Define and enforce data governance frameworks, including lineage, metadata, and compliance standards

Platform Integration & Enablement

Architect solutions for OCI-based ML Data Warehouse, feature stores, and curated datasets.
Enable interoperability between ML Ops pipelines, semantic layers, and BI platforms (Tableau Cloud, PowerBI).
Evaluate emerging technologies for natural language querying and reinforcement learning ecosystems.

Scalability & Performance Optimization

Design multi-cloud or hybrid architectures for resilience and cost efficiency.
Optimize infrastructure for high-performance analytics and ML workloads using containerization and orchestration tools.

Future-Ready Capabilities

Lay the foundation for advanced features such as automated retraining, semantic-driven insights, and natural language data querying.
Support integration of reinforcement learning and real-time analytics into the architecture.

Requirements:

Technical Expertise

5+ years in data or enterprise architecture roles with strong experience in analytics ecosystems.
Proficiency in OCI, Snowflake, Azure services, and BI platforms (Tableau Cloud, Power BI).
Familiarity with ML Ops concepts, feature stores, and CI/CD for analytics workloads.
Strategic & Leadership Skills

Ability to align architecture decisions with business objectives and roadmap priorities.
Strong collaboration skills across engineering, data science, and business teams.
Tools & Technologies
Understanding of semantic layer architecture and natural language query enablement.

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