Senior Data Architect

About the role

RQMNA is looking for a Senior Data Architect to own the end‑to‑end data backbone of its sovereign data platforms. You will design, build and govern the data architecture that powers national‑scale government and enterprise services.

Key responsibilities

• Define and maintain conceptual, logical and physical data models across the platform.
• Design multitenant data isolation, residency and classification in line with data protection laws.
• Architect a polyglot persistence strategy covering relational, graph, vector, document, time‑series, geospatial and object stores.
• Create the analytics and lakehouse layer that serves BI and AI workloads.
• Specify streaming and batch data movement, including CDC, event streaming and ETL/ELT pipelines.
• Operationalise data governance – cataloguing, lineage, quality, master data, retention and automated right‑to‑erasure.
• Define data security controls such as encryption, masking and activity monitoring.
• Author data standards, reference patterns and mentor data engineers across teams.

Required profile

• Proven experience building scalable, high‑integrity data systems that remain reliable under load.
• Deep knowledge of multiple modelling paradigms (relational, dimensional, Data Vault, graph, document).
• Strong expertise in SQL, performance tuning, partitioning, replication and high availability.
• Extensive background in data governance, lineage, classification, quality, master data and retention.
• Hands‑on experience with ETL, ELT, change‑data‑capture, event streaming and lakehouse architectures.
• Experience isolating tenants at scale and understanding vector/RAG layers for AI.
• Bachelor’s degree in Computer Science, Information Systems or related field; TOGAF or data platform certifications a plus.
• Fluent English required; Arabic is an advantage.

Required skills

• SQL
• Data modeling (relational, dimensional, Data Vault, graph, document)
• Performance tuning, partitioning, replication, high availability
• ETL / ELT
• Change data capture
• Event streaming
• Lakehouse architecture
• Data governance (cataloguing, lineage, quality, master data, retention)
• Data security (encryption, masking, activity monitoring)
• Polyglot persistence (relational, graph, vector, document, time‑series, geospatial, object stores)