Data Architect

Job Location: hybrid in Bucharest

Recruitment process:

• HR screening
• 2 X Technical Discussion

Role purpose

The role ensures that data is structured, governed, integrated and made available consistently across operational systems, SAP, the data platform, analytics and AI use cases.

The role provides architecture ownership for the new data platform while establishing sustainable principles for data domains, models, quality, lineage, ownership and lifecycle management across the wider application landscape.

Reporting relationships

Reports to: Head of Data and AI

The Data Architect is part of the permanent Architecture team and works within the enterprise architecture direction established by the Enterprise Architect Lead. Direct line-management responsibility remains with the Head of Data and AI.

Key responsibilities

Data architecture direction

• Define and maintain the enterprise data architecture, target state and transition roadmap.
• Establish principles for data domains, data products, master and reference data, metadata and lifecycle management.
• Define canonical and domain data models where common structures are required.
• Ensure data architecture supports operational, reporting, analytics and AI needs without unnecessary duplication.
• Identify data-related risks, technical debt and opportunities to simplify the landscape.

Data platform and solution design

• Provide architecture direction for the new data platform and its integration with SAP and other enterprise applications.
• Define patterns for data ingestion, transformation, storage, access and distribution.
• Review major data solution designs and ensure they align with the enterprise target architecture.
• Guide decisions on batch, streaming, analytical and operational data-processing approaches.
• Ensure platform designs are scalable, maintainable, observable and cost-conscious.

Data governance and quality

• Translate data-governance requirements into practical architecture standards and controls.
• Define expectations for data ownership, stewardship, quality, lineage, retention and classification.
• Work with business data owners to establish clear accountability for critical data.
• Ensure privacy, security and regulatory requirements are reflected in data architecture decisions.
• Make data quality and lineage visible for critical data flows and products.

Collaboration and delivery support

• Work with Process, Technical Domain, SAP and Integration Architects to align data decisions across the landscape.
• Support delivery teams with approved data patterns and early architecture guidance.
• Explain data architecture decisions and trade-offs in clear business language.
• Collaborate with data engineering, analytics, AI, security and platform teams.
• Ensure external partners transfer knowledge and do not retain permanent ownership of Müller's data architecture.

Decision authority

The Data Architect is authorised to:

• Define enterprise data architecture principles, standards and reusable patterns.
• Approve data designs that remain within the agreed enterprise direction.
• Require remediation or escalation where solutions create material data risk, duplication or non-compliance.
• Recommend data-platform investments and data-landscape simplification initiatives.
• Business ownership of data, investment decisions and material exceptions remain subject to the appropriate business, Data and AI, and architecture governance.

Required experience

• Significant experience in data architecture, data engineering or enterprise information management.
• Experience defining data architectures, models and transformation roadmaps in a complex organisation.
• Strong understanding of modern data platforms, data integration, metadata, quality and lineage.
• Experience with enterprise applications and SAP-related data landscapes.
• Practical experience with data governance, privacy, security and lifecycle requirements.
• Ability to guide delivery teams and external partners through complex data decisions.

Key capabilities

• Enterprise-wide data thinking and strong modelling skills.
• Ability to balance governance, usability, delivery speed and platform sustainability.
• Clear communication with business, data and technology stakeholders.
• Strong facilitation and decision-making across organisational boundaries.
• Ability to influence without relying on direct reporting authority.
• Pragmatic focus on reuse, quality and measurable business value.

Success measures

• A clear enterprise data architecture and roadmap are established and maintained.
• The new data platform follows common patterns and integrates coherently with SAP and the application landscape.
• Critical data has visible ownership, quality expectations and lineage.
• Data duplication and inconsistent definitions decrease over time.
• Delivery teams adopt approved data patterns and make decisions earlier.
• Architecture-related rework and dependency on external data consultants are reduced.