Job Description :

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 :

- 12+ 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.