Senior Data Architect

Role & Responsibilities

• Lead the design and implementation of enterprise-scale data architecture and cloud-native data platforms.
• Define enterprise data architecture standards, principles, reference architectures and technology roadmaps.
• Design and govern Data Lakehouse / Cloud Data Platform solutions using Snowflake, Databricks, Azure and AWS.
• Drive modernization of legacy data warehouses into scalable and cost-effective cloud data platforms.
• Define conceptual, logical and physical data models using Kimball, Data Vault, 3NF or hybrid approaches.
• Establish Data Governance, Data Quality, Metadata, Data Lineage, Data Catalog and MDM frameworks.
• Define and implement Data Product operating models, including ownership, lifecycle, roadmap, KPIs and adoption metrics.
• Design batch, real-time, streaming and event-driven data integration architectures.
• Provide architecture guidance across SQL, Python, PySpark, ETL/ELT and Kafka/streaming technologies.
• Provide secondary architecture support for AI/ML, MLOps, feature engineering, model deployment and monitoring.
• Review technical designs for scalability, security, resilience, maintainability, performance and cost.
• Lead architecture reviews and provide technical direction to Data Engineers, Architects and delivery teams.
• Conduct architecture workshops with business, security, governance and engineering stakeholders.
• Evaluate technology options, identify risks and communicate architecture trade-offs to senior management.
• Create architecture blueprints, decision records, governance standards and modernization roadmaps.

Preferred Candidate Profile

• 15+ years of experience in Enterprise Data Architecture with strong hands-on architecture ownership.
• Minimum 5+ years of experience with cloud-native data platforms.
• Strong experience with Snowflake, Databricks, Azure and AWS data services.
• Proven experience designing enterprise data platforms, Data Lakehouse and data modernization solutions.
• Strong knowledge of Data Governance, Data Quality, Metadata, Lineage, Data Catalog and MDM.
• Hands-on experience with Data Product management, including ownership, lifecycle, roadmap, KPIs and adoption metrics.
• Strong understanding of Kimball, Data Vault, Dimensional and 3NF data modelling.
• Strong technical knowledge of SQL, Python, PySpark, ETL/ELT and Kafka/Streaming.
• Working knowledge of AI/ML architecture and MLOps.
• Demonstrated ability to translate business requirements into scalable, governed and reusable data solutions.
• Strong stakeholder management and executive-level communication skills.
• Experience guiding engineering teams and governing implementation quality.
• Experience in large enterprise / multinational environments is preferred.
• Immediate availability and willingness to work 3 days per week from Electronic City, Bangalore preferred.