Lead Data Architect

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

The Lead Data Architect is a technical and strategic expert responsible for designing, optimizing, and overseeing the implementation of data architectures to meet the business and technical needs of the company. They play a key role in modernizing data infrastructures, ensuring their scalability, performance, and alignment with the overall strategy. They work closely with Data Engineering, Data Governance, IT, and business teams to translate functional requirements into robust architectural solutions, while mentoring junior architects and promoting best practices.

Responsibilities and Missions

1. Design and Optimize Data Architectures

Define data architectures (data lakes, data warehouses, data mesh, data fabric) tailored to business and technical needs.
Model data (relational, NoSQL, graph) to support analytics, AI, and operational processes.
Evaluate and select technologies (e.g., Snowflake, Databricks, Kafka, Collibra) based on performance, cost, and scalability criteria.
Architect hybrid or multi-cloud solutions to ensure flexibility and resilience.

2. Drive the Modernization of Data Infrastructures

Migrate legacy systems to modern architectures (cloud, microservices, serverless).
Optimize the performance of data platforms (e.g., query tuning, partitioning, caching).
Automate deployments (IaC with Terraform, Ansible) and data management processes.
Integrate streaming solutions (Kafka, Flink) to enable real-time processing.

3. Ensure Data Governance and Security

Define governance standards (metadata, lineage, quality) in collaboration with Data Governance teams.
Apply security best practices (encryption, access control, GDPR compliance).
Document architectures and technical decisions to ensure traceability and maintainability.
Collaborate with the CISO to integrate security by design (privacy by design).

4. Collaborate with Data Engineering and Data Science Teams

Work with Data Engineers to ensure pipelines comply with architectural standards.
Support Data Scientists in designing solutions tailored to their needs (e.g., feature stores, sandboxes).
Participate in defining use cases to translate business needs into technical requirements.
Facilitate the integration of AI models into existing architectures.

5. Mentor and Train Team Members

Guide data architects and engineers in solving complex problems and adopting best practices.
Review designs and code to ensure the quality and consistency of solutions.
Organize workshops and training on new technologies (e.g., data mesh, Data Vault 2.0).
Participate in recruiting and onboarding new team members.

6. Innovate and Continuously Improve Architectures

Evaluate and propose new technologies (e.g., lakehouse, data fabric, generative AI).
Optimize infrastructure costs by identifying bottlenecks and efficiency opportunities.
Contribute to the technical roadmap by proposing improvements based on field feedback.
Participate in innovation projects (e.g., real-time data platform deployment, generative AI integration).

Examples of Concrete Achievements

Designed a data mesh architecture for an international group, reducing team dependencies by 50% and improving agility.
Migrated a data warehouse to Snowflake, reducing infrastructure costs by 25% and improving query performance by 40%.
Optimized a data lake by implementing partitioning and compression strategies, reducing storage costs by 30%.
Architected a real-time data platform with Kafka and Flink, enabling instant transaction analysis for fraud detection.
Mentored 4 junior data architects, helping them upskill in cloud architectures and modeling best practices.