Data Architect

12 - 17 years

13 - 17 Lacs

Posted:5 days ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

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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