Senior Data Architect

(Member of the Data Architecture team, reporting to the Lead Data Architect or Head of Data Architecture)

Recruiting a Senior 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 Senior Data Architect is an individual technical expert responsible for designing, optimizing, and implementing data architecture solutions to address the company’s complex needs. As a key contributor to the team, they focus on data modeling, system integration, and infrastructure optimization, ensuring alignment with the overall strategy defined by the Lead Data Architect. They work closely with Data Engineering, Data Governance, and business teams to translate functional requirements into robust technical solutions, while promoting best practices and solving complex architectural challenges.

Responsibilities and Missions

1. Design and Implement Data Architecture Solutions

Model complex data structures (relational, NoSQL, graph) to support analytics, AI, and business processes.
Develop reference architectures for specific use cases (e.g., data mesh, data fabric, hybrid solutions).
Evaluate and recommend technologies (e.g., Snowflake, Databricks, Kafka) based on performance, scalability, and cost requirements.
Document architectural designs and technical decisions to ensure traceability and maintainability.

2. Optimize Data Infrastructures and Pipelines

Improve the performance of existing data platforms (e.g., query tuning, storage optimization, partitioning).
Automate data management processes (e.g., IaC deployments, CI/CD pipelines).
Integrate streaming solutions (Kafka, Flink) to enable real-time processing.
Migrate legacy systems to modern architectures (cloud, microservices).

3. Ensure Data Quality and Compliance

Define and apply quality standards (schema validation, cleansing, lineage).
Collaborate with Data Governance teams to ensure architectures comply with regulations (GDPR, ISO 27001).
Implement security controls (encryption, access management) in architectural designs.
Participate in audits to verify compliance of solutions.

4. Collaborate with Technical and Business Teams

Work with Data Engineers to ensure pipelines adhere to architectural standards.
Support Data Scientists in designing solutions tailored to their needs (e.g., feature stores, testing environments).
Translate business requirements into clear technical specifications for development teams.
Facilitate the integration of AI models into existing architectures.

5. Promote Best Practices and Innovate

Evaluate and propose emerging technologies (e.g., data fabric, lakehouse, advanced streaming tools).
Optimize infrastructure costs by identifying bottlenecks and efficiency opportunities.
Participate in innovation projects (e.g., real-time data platform deployment, generative AI integration).
Contribute to technological watch and share knowledge with the team.

6. Solve Complex Problems and Provide Occasional Mentoring

Diagnose and resolve architectural issues (e.g., latencies, data inconsistencies, bottlenecks).
Propose creative solutions for specific technical challenges.
Participate in code and design reviews to ensure solution quality.
Provide occasional mentoring to junior team members on advanced technical topics.

Examples of Concrete Achievements

Designed and implemented a data mesh architecture for a specific business domain, reducing data access times by 40% and improving data quality.
Optimized a data warehouse by reorganizing schemas and implementing partitioning strategies, reducing storage costs by 25% and improving query performance.
Developed a real-time integration solution with Kafka and Spark, enabling instant analysis of IoT data for predictive maintenance.
Migrated a legacy system to a cloud-native architecture, reducing infrastructure costs by 20% while improving scalability.
Solved a complex latency issue in a critical pipeline, reducing processing times by 70% through architectural redesign.