Data Architect

Wishtree Technologies; Michigan; On-site; USD 150,000 - 210,000; Full time

Job summary

Wishtree Technologies is seeking a senior Data Architect to define and govern the enterprise data architecture strategy. You will design scalable data platform architectures to support analytics, AI/ML, and enterprise applications across the organization.

You will lead data modeling, data integration, and governance efforts, partnering with SAP/ERP teams and data engineers to modernize our cloud data landscape and ensure secure, scalable data services.

Qualifications

• Strong experience in Enterprise Data Architecture.
• Knowledge of data modeling concepts (conceptual, logical, physical).
• Experience with cloud data platforms (Azure, GCP, Databricks).
• Experience with SAP data integration and enterprise systems.
• Ability to lead architecture reviews and governance initiatives.
• Strong stakeholder management and communication skills.

Responsibilities

• Define and maintain enterprise data architecture strategy, standards, and reference architectures.
• Design scalable Data Platform architectures supporting analytics, AI/ML, and enterprise applications.
• Develop data models (conceptual, logical, physical) and data architecture patterns.
• Establish data governance frameworks, policies, standards, and operating models.
• Collaborate with SAP, ERP, CRM teams to integrate data into the enterprise platform.
• Lead architecture reviews, solution governance, and modernization initiatives.

Skills

Enterprise Data Architecture

Data Modeling

Cloud Data Platforms

Data Governance

Data Integration

Stakeholder Management

Education

Bachelor's degree in a relevant field

Tools

Databricks

Azure

SAP data integration

Data Catalog tools

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:
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
  

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Experience with SAP data and enterprise system integrations.
• 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.
• Knowledge of SAP S/4HANA and SAP data integration.
• 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.

Leadership & Behavioral Competencies

• 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.