Data Architect

We are seeking an experienced Data Architect to lead the design and implementation of a modern enterprise data platform built on Azure Databricks. The successful candidate will define the target-state data architecture, establish enterprise data modeling standards, and design scalable data integration and governance frameworks aligned with business objectives. The role combines strategic architecture leadership with hands‑on involvement in solution design, technical reviews, and implementation guidance. The Data Architect will work closely with business stakeholders, Data Engineers, Analytics teams, and Enterprise Architects to deliver a secure, scalable, high‑performance Lakehouse platform that supports enterprise reporting, analytics, and future business needs.

Key Responsibilities
1. Data Architecture and Solution Design
- Define and maintain the target-state data architecture for the enterprise data platform.
- Design scalable Lakehouse architectures using Azure Databricks and industry best practices.
- Develop conceptual, logical, and physical data models aligned with business and technical requirements.
- Establish enterprise standards for data modeling, data integration, data storage, and data governance.
- Define architecture patterns for data ingestion, transformation, consumption, and integration across enterprise systems.
- Ensure architecture designs meet requirements for scalability, performance, security, reliability, and cost efficiency.
- Guide platform evolution and contribute to the enterprise data architecture roadmap. 2. Enterprise Data Modeling
- Lead enterprise-wide data modeling activities and define reusable modeling standards.
- Translate business requirements into scalable and maintainable data models.
- Define enterprise data structures, business entities, relationships, and common business definitions.
- Design data models for Data Warehouses, Data Lakes, Lakehouse platforms, and analytical consumption layers.
- Review and approve solution‑specific data models developed by Data Engineers and delivery teams.
- Promote consistency and reuse of data models across business domains and data products.
- Ensure data models support reporting, analytics, data integration, and future business requirements. 3. Data Governance, Security, and Compliance
- Define and implement enterprise data governance principles, standards, and architecture guidelines.
- Establish practices for data lineage, metadata management, data cataloging, and data ownership.
- Define data access patterns, security models, and role‑based access control.
- Ensure alignment with enterprise security policies, regulatory requirements, and data protection standards.
- Support the implementation of data governance capabilities within the Databricks Lakehouse environment.
- Promote data quality, consistency, traceability, and trusted data consumption across the platform. 4. Databricks and Azure Data Platform Architecture
- Design and guide the implementation of enterprise data solutions using Azure Databricks.
- Define architectural patterns for Lakehouse storage, data processing, integration, and analytical consumption.
- Establish best practices for data organization, workload management, performance optimization, and platform scalability.
- Guide Data Engineers in implementing architecture standards and reusable technical components.
- Evaluate platform capabilities and recommend appropriate technologies and design patterns.
- Support technical decision‑making related to Azure cloud services and enterprise data platform components. 5. Delivery Support and Technical Leadership
- Work closely with Data Engineers to translate architecture designs into production‑ready solutions.
- Review technical designs, data models, integration patterns, and architecture decisions.
- Provide guidance on performance optimization, scalability, maintainability, and cost management.
- Identify architectural risks, dependencies, and opportunities for platform improvement.
- Ensure implementation aligns with target architecture and established engineering standards.
- Support delivery planning and contribute to the continuous improvement of the data platform. 6. Stakeholder Management and Collaboration
- Conduct business and technical workshops to gather requirements and define architecture solutions.
- Collaborate with business stakeholders, Enterprise Architects, Data Engineers, BI teams, and Analytics teams.
- Translate complex technical concepts into clear recommendations for technical and non‑technical audiences.
- Communicate architecture decisions, trade‑offs, risks, and recommendations effectively.
- Act as a trusted advisor on enterprise data architecture, data modeling, and platform strategy.
- Ensure architecture decisions align with business priorities and long‑term enterprise data objectives. Mandatory Skills and Experience
- Minimum 7+ years of experience in Data Architecture.
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