Lead Enterprise - Data Architect

Lead Enterprise - Data Architect

Functional Role: Lead Enterprise Data Architect

Primary Skill: Enterprise Data Architecture

Seniority: Senior / Lead

Location: All Romania

Language: English

Core Expertise: Enterprise Data Architecture, Data Strategy, Enterprise Data Model, Data Governance, Data Debt Management, Database Architecture, Data Migration & Modernization, Data Modelling, Data Integration, Architecture Standards & Governance.

Role Overview

We are looking for a Lead Enterprise Data Architect to provide strategic and technical leadership for the organization's enterprise data architecture.

The role is responsible for defining the Data Architecture Strategy, Reference Architecture, principles, standards, patterns, and governance frameworks, ensuring that the organization's data architecture is aligned with business strategy and objectives.

A key responsibility is to eliminate and contain Data Debt by governing in-flight projects, enforcing data modelling and documentation best practices, reverse-engineering production applications, and developing a comprehensive Enterprise Data Model and central Enterprise Data Repository.

The successful candidate will operate at enterprise level, working closely with business leaders, Enterprise Architects, Data Engineers, Data Scientists, IT teams, and other stakeholders. Strong communication, articulation, influencing, and stakeholder-management skills are essential.

Key Responsibilities

Enterprise Data Architecture & Strategy

• Define and maintain the Enterprise Data Architecture Strategy and Vision.
• Establish Data Architecture principles, standards, guidelines, patterns, and reference architectures.
• Align the Enterprise Data Architecture with the organization's business strategy, vision, and objectives.
• Promote the concept of Data as an Asset and drive the organization toward strategic data alignment.
• Map Enterprise Data Architecture to Enterprise Information Management capabilities.
• Provide architectural guidance for enterprise-wide data design, development, implementation, and modernization initiatives.
• Ensure data is structured, stored, accessed, integrated, and managed effectively across the enterprise.

Enterprise Data Model & Data Repository

• Develop and maintain the Enterprise Data Model, including conceptual, logical, and physical data models.
• Build and maintain a common Enterprise Data Repository and map it to the Enterprise Meta-Model.
• Establish consistent relationships between enterprise data architecture and enterprise information management.
• Maintain continuity between data architecture and enterprise architecture repositories.
• Clone or synchronize relevant Data References between the EA Architecture tool and Data Architecture tool, where applicable.
• Establish reusable enterprise data definitions, patterns, models, and standards.

Data Debt Management

• Establish an enterprise approach for identifying, eliminating, and containing Data Debt.
• Govern in-flight projects to ensure compliance with approved data modelling, architecture, and documentation standards.
• Review project architectures and ensure appropriate data design practices are followed.
• Reverse-engineer existing applications and production environments to identify undocumented data structures, dependencies, and flows.
• Identify gaps between the current-state data landscape and the target Enterprise Data Architecture.
• Define remediation and modernization approaches for legacy data architectures.

Data Architecture Standards & Governance

• Establish data architecture standards, technology standards, principles, and best practices.
• Define standards covering different database technologies, including relational, NoSQL, analytical, and other enterprise database platforms.
• Develop and maintain SOPs for technology standardization and ensure adoption across business units.
• Establish a Technology Audit framework and process for data architecture and database standards.
• Establish and enable an enterprise Data Governance framework and operating processes.
• Define and enforce policies related to data quality, security, ownership, accessibility, and compliance.
• Ensure alignment with regulatory requirements, including GDPR.
• Obtain and maintain stakeholder and business-owner buy-in for data governance policies and standards.

Data Migration & Modernization

• Define enterprise data migration and modernization strategies, approaches, and best practices.
• Establish principles for migration from legacy technologies toward rationalized enterprise standards.
• Define migration patterns, assessment approaches, target-state architectures, and transition strategies.
• Provide architecture guidance for database modernization and technology rationalization.
• Support business units in adopting standardized approaches for data migration and modernization.

Data Modelling

• Lead enterprise data modelling activities across business and technology domains.
• Translate business requirements into data models supporting both operational and analytical processes.
• Develop and govern:
• • Conceptual Data Models
  • Logical Data Models
  • Physical Data Models
  • Enterprise Data Models
  • Data Flow Models
  • Data Domain Models
• Ensure models provide appropriate levels of performance, scalability, maintainability, and interoperability.
• Establish modelling standards and ensure project teams follow approved practices.
• Use enterprise architecture and data modelling tools to create and maintain architecture artefacts.

Data Integration

• Define and oversee enterprise data integration architecture.
• Design and govern solutions integrating data from internal and external sources.
• Ensure consistent and reliable data flows across applications, databases, platforms, and analytical environments.
• Establish integration patterns and best practices for data movement and interoperability.
• Ensure data integration architectures align with enterprise standards and target-state architecture.

Technology Evaluation

• Monitor emerging data architecture, database, cloud, analytics, and integration technologies.
• Evaluate new technologies and assess their applicability within the enterprise.
• Recommend technologies and solutions that improve data capabilities, scalability, efficiency, and governance.
• Contribute to technology rationalization and standardization initiatives.

Architecture Documentation

• Establish and maintain comprehensive Data Architecture documentation.
• Maintain:
• • Enterprise Data Models
  • Data Flow Diagrams
  • Reference Architectures
  • Architecture Decision Records
  • Technology Standards
  • Data Architecture Patterns
  • Technical Specifications
  • Migration Strategies
  • Governance Documentation
• Ensure architecture documentation remains accurate, consistent, and accessible to relevant stakeholders.

Technical Leadership & Mentoring

• Provide technical leadership to Data Engineering, Data Architecture, and related technology teams.
• Mentor architects, data engineers, and technical specialists on data modelling and architecture best practices.
• Establish a culture of architectural discipline and continuous improvement.
• Provide guidance and architectural decisions for complex enterprise data initiatives.

Stakeholder & Business Engagement

• Work closely with business leaders, business owners, Enterprise Architects, Data Engineers, Data Scientists, IT teams, and other stakeholders.
• Translate complex data architecture concepts into clear business language.
• Facilitate architecture discussions and decision-making with senior stakeholders.
• Drive consensus across different business and technology areas.
• Present architecture strategies, standards, risks, and recommendations to senior management.
• Establish strong relationships with key stakeholders and business owners.

Required Technical Skills & Experience

Enterprise Data Architecture

• Extensive experience in Enterprise Data Architecture within large and complex organizations.
• Proven experience defining Data Architecture Strategy, Vision, Reference Architecture, principles, standards, and guidelines.
• Strong understanding of Enterprise Architecture and Enterprise Information Management.
• Experience developing enterprise-wide target-state data architectures.

Data Modelling

• Expert knowledge of conceptual, logical, and physical data modelling.
• Proven experience developing and governing Enterprise Data Models.
• Strong understanding of data domains, entities, relationships, metadata, and data flows.
• Experience with enterprise data modelling and architecture tools such as:
• • Hackolade
  • ERwin
  • PowerDesigner
  • Rational
  • Other Enterprise Architecture / Data Architecture tools

Database Architecture

• Strong knowledge of database architecture and technology standards.
• Experience across multiple database types, including:
• • Relational databases
  • NoSQL databases
  • Analytical databases
  • Cloud databases
  • Data warehouse technologies
• Strong SQL knowledge.
• Understanding of database performance, scalability, security, and technology rationalization.

Cloud & Data Technologies

• Strong understanding of enterprise data architectures across AWS, Azure, and/or GCP.
• Knowledge of modern data platforms and big-data technologies.
• Understanding of technologies such as Hadoop and Spark.
• Knowledge of ETL/ELT architectures and data integration patterns.
• Working knowledge of Python and its application within data environments.

Data Governance & Security

• Strong understanding of Data Governance frameworks and operating models.
• Experience establishing policies and processes for:
• • Data quality
  • Data ownership
  • Data security
  • Metadata
  • Data lifecycle
  • Regulatory compliance
• Knowledge of GDPR and data protection principles.
• Experience establishing architecture and technology audit processes.

Tools & Platforms

Experience with one or more of the following is highly desirable:

• Hackolade
• ERwin
• PowerDesigner
• Enterprise Architecture tools
• Bizzdesign
• Collibra
• Data catalog / metadata management platforms
• Data governance platforms

Bizzdesign and Collibra experience will be considered a strong advantage.

Nice-to-Have Skills

• Experience leading enterprise-wide Data Debt reduction initiatives.
• Experience reverse-engineering legacy production applications.
• Experience with large-scale data migration and modernization programs.
• Experience establishing enterprise database technology standards.
• Experience with SAP data architecture and/or other large enterprise application ecosystems.
• Experience with data mesh, data fabric, lakehouse, or modern enterprise data architecture concepts.
• Knowledge of information architecture and metadata management.
• Experience with automated data lineage and data discovery.
• Experience in regulated industries or large multinational organizations.

Key Competencies

• Enterprise Data Architecture
• Data Architecture Strategy & Vision
• Enterprise Data Modelling
• Data Governance
• Data Debt Management
• Database Architecture
• Data Migration & Modernization
• Data Integration
• Data Standards & Technology Rationalization
• Enterprise Architecture
• Data Security & GDPR
• Architecture Governance
• Strategic Thinking
• Stakeholder Management
• Leadership & Mentoring
• Communication & Articulation
• Influencing & Negotiation
• Analytical & Problem-Solving Skills

Seniority Expectations – Senior / Lead

This is a senior-level / lead Enterprise Architecture position requiring the ability to operate at both strategic and technical levels.

The successful candidate should be capable of:

• Defining enterprise-wide Data Architecture strategy and direction.
• Establishing architecture standards and governance frameworks.
• Making and defending architectural decisions with senior stakeholders.
• Governing data architecture across multiple projects and business units.
• Leading Enterprise Data Modelling initiatives.
• Driving Data Debt reduction and modernization.
• Challenging non-compliant project architectures and influencing corrective action.
• Building consensus between business and technology stakeholders.
• Mentoring architecture and data engineering teams.
• Communicating complex technical concepts clearly to business owners and executive stakeholders.