Manager - Data Platform Architect

About the role:

The Data Platform Architect plays a strategic role in designing, securing, and scaling Diageos global data ecosystem the Diageo Data Hub (DDH).

This role combines deep expertise in cloud architecture, data frameworks, and security design with architectural leadership to build a trusted, high-quality, and compliant data platform that powers analytics, AI, and decision-making across markets.

The architect will define end-to-end platform capabilities, ensuring alignment with enterprise security standards, architectural principles, and modernization goals enabling scalable, automated, and governed data solutions across Diageos business landscape.

Role Responsibilities:

1. Platform & Cloud Architecture

• Design and evolve the global data platform architecture (Azure + Databricks + Unity Catalog + Power BI), ensuring scalability, performance, and cost efficiency.
• Define cloud architecture standards across compute, storage, and networking layers, leveraging Azure best practices.
• Collaborate with enterprise architects to ensure platform alignment with cloud-native and hybrid models.

2. Security Architecture & Compliance

• Define and implement security architecture for data assets covering data encryption, access control, identity management, and data masking.
• Work closely with information security and compliance teams to ensure adherence to GDPR, data privacy, and internal data classification standards.
• Embed zero-trust principles and role-based access controls (RBAC) across all platform components (Databricks, Synapse, ADLS, Power BI, etc.).
• Champion data governance and lineage visibility via integration with Unity Catalog and metadata systems.

3. Data Framework & Platform Capabilities

• Design and drive adoption of reusable frameworks for data ingestion, transformation, quality, and observability.
• Establish platform accelerators for data quality rules, data contracts, monitoring, and alerting.
• Lead modernization and automation initiatives using CI/CD pipelines, infrastructure-as-code, and metadata-driven orchestration.
• Ensure frameworks are optimized for performance, reusability, and market-specific scalability.

4. Solution Design & Review

• Review and approve solution designs from engineering teams, ensuring adherence to architectural guardrails and platform standards.
• Act as a design authority for projects integrating with the DDH ecosystem.
• Provide mentorship and guidance to engineers and architects in adopting standardized design patterns.

5. Collaboration & Innovation

• Partner with data governance, DevOps, and security teams to strengthen data trust, observability, and automation.
• Research and pilot emerging technologies (e.g., GenAI, data mesh, semantic layers, AI workbenches) to enhance data democratization and self-service analytics.
• Contribute to platform maturity through continuous R&D and capability benchmarking.

Experience / skills required:

Technical Expertise

• Proven experience with Azure Data Platform components ADLS, Databricks, Key Vault, , ADF, etc.
• Strong understanding of data engineering principles, orchestration tools (ADF, workflow), and data mesh architecture.
• Experience with Unity Catalog, metadata management, data lineage, and governance frameworks.
• Proficiency in Python, SQL, and Spark for framework or proof-of-concept development.
• Familiarity with DevOps, CI/CD pipelines, and IaC (Terraform, ARM templates).

Architecture & Security Skills

• Deep understanding of cloud and security architecture patterns encryption at rest/in transit, RBAC, network security, private endpoints, etc.
• Ability to translate business requirements into scalable, secure, and reusable platform solutions.
• Experience defining architecture blueprints, reference models, and reusable design assets.

Soft Skills

• Strong communication and stakeholder management abilities across technical and business teams.
• Strategic thinker with a pragmatic approach to platform evolution.
• Passionate about innovation, simplification, and driving data excellence at scale.