Data Architect (Contractual)

Wishtreetech; Maharashtra; Hybrid; INR 2,500,000 - 4,200,000; Full time

Job summary

Wishtreetech is seeking a Senior Enterprise Data Architect in Maharashtra to define and govern the organization’s data architecture strategy. You will design scalable cloud-native data platforms (Azure, GCP, Databricks) and lead governance across data lakes, warehouses, and lakehouses.

The role requires translating business needs into robust data solutions while guiding modernization efforts. You will collaborate with business leaders and tech teams to ensure security, compliance, and alignment

Qualifications

• Strong experience in Enterprise Data Architecture across conceptual, logical, and physical layers.
• Deep knowledge of data modeling, governance, metadata management, and data lineage.
• Experience designing and governing Enterprise Data Platforms on Azure, GCP, and Databricks.
• Ability to translate business requirements into scalable, modern data solutions.
• Track record leading architecture reviews and governance for large programs.

Responsibilities

• Define and maintain the enterprise data architecture strategy, principles, standards, and reference architectures.
• Design scalable Enterprise Data Platform architectures supporting analytics, reporting, AI/ML, and enterprise apps.
• Develop data architectures and data models across conceptual, logical, and physical layers.
• Define data architecture patterns for ingestion, integration, storage, processing, serving, analytics, and consumption.
• Establish standards for data modeling, integration, storage, lifecycle, and interoperability.
• Ensure architecture decisions align with technology strategy and business objectives.
• Conduct architecture assessments and identify modernization opportunities.
• Architect and govern cloud data platforms using Azure, GCP, and Databricks.
• Define patterns for data lakes, data warehouses, lakehouses, and modern data platforms.
• Evaluate cloud data services and select technologies based on scale, cost, security, and needs.
• Ensure cloud data platforms are highly available, scalable, and cost-optimized.
• Establish standards for deployment and operation of cloud data platforms.
• Define, implement, and enforce enterprise Data Governance frameworks, policies, and models.
• Establish governance for ownership, stewardship, classification, access, and lifecycle.
• Enforce data architecture and governance standards across projects and partners.
• Manage critical data assets and business datasets.
• Ensure governance aligns with security, privacy, regulatory, and compliance requirements.
• Provide metadata management, data catalog, and data lineage strategies.
• Ensure end-to-end data lineage visibility across systems and transforms.
• Support adoption of data catalog and metadata management solutions.
• Improve data discoverability, traceability, and understanding across the org.
• Provide architecture leadership for Data Modernization, Analytics, AI/ML, and ERP modernization programs.
• Translate business requirements into scalable architecture solutions.
• Define target-state architecture and transformation roadmaps.
• Support migration from legacy to modern cloud architectures.
• Ensure data architecture supports future AI/GenAI initiatives.

Skills

Enterprise Data Architecture

Data Modeling

Data Governance

Cloud Data Platforms

Data Lakes & Warehouses

Metadata Management

Data Integration & ETL/ELT

Security & Compliance

Stakeholder Management

AI/GenAI data architectures

Job description

• 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.
• 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. 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.
• 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.

5. 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.
• 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.

7. 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.

8. 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.

9. Stakeholder & Partner Management

• Collaborate with business leaders, enterprise architects, data engineering teams, security teams, IT teams, and implementation partners .
• Facilitate architecture workshops and design discussions with senior stakeholders.
• Communicate complex technical concepts clearly to both technical and non-technical audiences.
• Provide technical leadership and direction to internal teams and external implementation partners.
• Drive alignment between business objectives, technology strategy, and data architecture.
• Manage architecture-related dependencies, risks, and escalations across enterprise programs.

Required Technical Skills

Must-Have

• Strong experience in Enterprise Data Architecture .
• Strong knowledge of Data Modeling – conceptual, logical, and physical.
• Experience designing and governing Enterprise Data Platforms .
• Strong experience with one or more cloud data platforms:
  • Azure
  • GCP
  • Databricks
• Strong understanding of Data Governance frameworks and operating models .
• Experience with Data Quality frameworks and management .
• Strong knowledge of Metadata Management, Data Catalog, and Data Lineage .
• Strong understanding of Data Lake, Data Warehouse, and Lakehouse architectures .
• Experience with enterprise-scale data integration and ETL/ELT architectures .
• Strong understanding of Cloud Architecture, Data Security, and Compliance .
• Experience leading architecture reviews and solution design governance .
• Strong stakeholder management and communication skills.

Good to Have

• Experience with Azure Data Factory / Synapse / Microsoft Fabric .
• Experience with Databricks Lakehouse architecture .
• Experience with GCP BigQuery / Dataflow / Dataproc .
• Experience with enterprise Data Catalog / Metadata tools such as Microsoft Purview, Collibra, Alation, or equivalent .
• Knowledge of Master Data Management (MDM) .
• Experience with Data Mesh / Data Fabric concepts.
• Knowledge of API-led integration and event-driven architectures .
• Exposure to AI/ML and Generative AI data architectures .
• Experience with large-scale ERP modernization or cloud migration programs .
• Strong architecture leadership and decision‑making ability.
• Excellent stakeholder management and communication skills.
• Ability to influence architecture decisions across multiple teams and business functions.
• Strong analytical and problem‑solving skills.
• Ability to work effectively with senior leadership, architects, engineering teams, vendors, and system integrators .
• Ability to manage multiple enterprise initiatives and competing priorities.
• Strong understanding of business requirements and ability to translate them into technology solutions.
• Comfortable working in complex, global, and transformation‑driven environments.

Required Skills