Head of Data Architecture

Recruiting a Head of Data Architecture requires a thorough understanding of the role. The following is a very general summary, which should be adapted to your specific context.

The Head of Data Architecture is responsible for designing, governing, and evolving enterprise data architectures. Their mission is to define a coherent, scalable vision for data infrastructures that supports the company’s strategic objectives and digital transformation goals.

They oversee data modeling, system integration, and platform modernization (data lakes, data warehouses, data mesh) to ensure that data is accessible, secure, and actionable for all stakeholders. In close collaboration with IT, Data Engineering, Data Governance, and business teams, they ensure the data architecture supports both current needs and future requirements in analytics, AI, and automation.

Responsibilities and Missions

1. Define the Data Architecture Strategy and Roadmap

Develop a comprehensive data architecture vision, aligned with business strategy and priorities.
Establish a technological roadmap (cloud migration, adoption of data mesh, modernization initiatives).
Evaluate and select technologies and tools (e.g., Snowflake, Databricks, Collibra) based on scalability, performance, and cost.
Collaborate with management to prioritize investments according to business impact.

2. Design Scalable and High-Performance Data Architectures

Model data (relational, NoSQL, graph) to meet analytical and operational needs.
Define integration standards between systems (ERP, CRM, IoT) and data platforms (data lakes, warehouses).
Architect solutions to support AI and machine learning (feature stores, MLOps pipelines).
Ensure data consistency across environments (dev, prod, sandbox).

3. Oversee Data Governance and Security

Define and enforce governance rules (metadata, lineage, quality) with Data Governance teams.
Ensure compliance with security and regulatory standards (encryption, access control, GDPR).
Implement access management and auditing to monitor data usage.
Work with the CISO to integrate security by design (privacy by design).

4. Drive the Modernization of Data Infrastructures

Lead the migration of legacy systems to modern architectures (cloud, microservices, serverless).
Optimize costs and performance (right-sizing, auto-scaling).
Automate deployments and infrastructure management using Infrastructure-as-Code (Terraform, Ansible).
Evaluate adoption of innovative technologies (data fabric, lakehouse, real-time streaming).

5. Collaborate with Data Engineering and Data Science Teams

Ensure data pipelines comply with architectural standards.
Support Data Scientists in building tailored solutions (feature stores, sandbox environments).
Translate business use cases into technical requirements.
Facilitate the integration of AI models into enterprise architectures.

6. Evangelize and Train Teams on Best Practices

Promote data architecture awareness among IT, business, and data teams.
Train architects and engineers in modern methodologies (data mesh, Data Vault 2.0).
Document architectural decisions and maintain a clear reference framework.
Organize architecture workshops and reviews to foster continuous improvement.

Examples of Concrete Achievements

Designed and deployed a data mesh architecture, reducing cross-team dependencies by 60% and improving agility.
Migrated an on-premise data warehouse to Snowflake, cutting infrastructure costs by 30% while boosting query performance.
Defined a Data Vault 2.0 strategy for an international group, enabling seamless integration of 15 data sources and reducing reporting delivery time.
Architected a real-time data platform with Kafka and Flink, enabling instant fraud detection and transaction monitoring.
Established enterprise-wide governance standards (lineage, metadata, quality), adopted by 1,000+ users, improving trust, compliance, and adoption.