Data Architect

1 Year On-Site - Bangalore followed by Remote
1. 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.

Work arrangement
No