1 Year In office - Bangalore followed by Remote

- Enterprise Data Architecture

- Define and maintain the enterprise data architecture strategy, principles, standards, and reference architectures.
- Design scalable Enterprise Data Platform (EDP) architectures supporting analytics, reporting, AI/ML, and enterprise applications.
- Develop conceptual, logical, and physical data architectures and data models.
- Define data architecture patterns covering data ingestion, integration, storage, processing, serving, analytics, and consumption.
- Establish standards for data modeling, data integration, data storage, data lifecycle, and data interoperability.
- Ensure architecture decisions align with enterprise technology strategy and business objectives.
- Conduct architecture assessments and identify opportunities for modernization and optimization.

2.

Cloud Data Platform

Architecture

- Architect and govern cloud-based data platforms using technologies such as:
- Microsoft Azure
- Google Cloud Platform (GCP)
- Databricks
- Define architecture patterns for data lakes, data warehouses, lakehouses, and modern data platforms.
- Evaluate cloud data services and recommend appropriate technologies based on scalability, performance, cost, security, and business requirements.
- Ensure cloud data platforms are designed for high availability, scalability, resilience, performance, and cost optimization.
- Establish standards for cloud data platform deployment and operationalization.

3. SAP &
- Enterprise System Integration

- Define architecture for integrating data from SAP and other enterprise applications into the Enterprise Data Platform.
- Work with SAP, ERP, CRM, and other enterprise system teams to understand source systems and data structures.
- Design robust batch and real-time data integration patterns.
- Ensure data integration architectures support enterprise transformation and ERP modernization initiatives.
- Evaluate integration approaches, interfaces, APIs,



ETL/ELT pipelines, and data exchange mechanisms.

4. Data Governance
- Define, implement, and enforce enterprise-wide Data Governance frameworks, policies, standards, and operating models.
- Establish governance processes for data ownership, stewardship, classification, access, usage, retention, and lifecycle management.
- Define and enforce data architecture and governance standards across projects and implementation partners.
- Establish processes for managing critical data assets and business-critical datasets.
- Ensure governance practices align with organizational security, privacy, regulatory, and compliance requirements.

5. Metadata, Lineage &
- Data Catalog

- Define enterprise strategies for Metadata Management, Data Cataloging, and Data Lineage.
- Establish standards for technical, business, and operational metadata.
- Ensure end-to-end visibility of data lineage across source systems, data platforms, transformations, and consumption layers.
- Support implementation and adoption of enterprise data catalog and metadata management solutions.
- Improve data discoverability, traceability, and understanding across the organization.

6.

Data Quality

Management

- Define enterprise Data Quality frameworks, standards, KPIs, and processes.
- Establish data quality rules covering accuracy, completeness, consistency, timeliness, uniqueness, and validity.
- Implement mechanisms for continuous monitoring and reporting of data quality.
- Work with business data owners and technology teams to identify and resolve critical data quality issues.




- Ensure data quality requirements are incorporated into data platform and integration designs.

7.

Architecture

Review &

- Solution Governance

- Lead and conduct architecture reviews for data platform and transformation initiatives.
- Review solution designs, technical architecture documents, data models, integration designs, and technology selections.
- Validate proposed solutions against enterprise architecture, security, governance, performance, and scalability standards.
- Identify architectural risks, dependencies, gaps, and opportunities for improvement.
- Provide architecture guidance and technical direction to engineering teams and implementation partners.
- Ensure deviations from enterprise standards are properly assessed, documented, and approved.

8.

Enterprise

Transformation &

- Modernization

- Provide data architecture leadership for large-scale Enterprise Transformation, Data Modernization, Analytics, AI/ML, and ERP modernization programs.
- Translate business requirements into scalable enterprise data architecture solutions.
- Work with business and technology leadership to define target-state architecture and transformation roadmaps.
- Support migration from legacy data platforms to modern cloud-based architectures.
- Ensure data architecture supports future AI, GenAI, analytics, and digital transformation initiatives.

9. Security, Compliance &
- Risk

- Ensure enterprise data architectures adhere to security, privacy, regulatory, and compliance requirements.
- Define appropriate approaches for data access control, encryption, data classification, and secure data sharing.
- Work closely with security and risk teams to address data-related security requirements.
- Identify and mitigate architecture and data governance risks.
- Ensure sensitive and critical data is appropriately protected throughout its lifecycle.

📌 Data Architect (Bengaluru)
🏢 Wishtree Technologies
📍 Bengaluru