Staff Data Engineering Architect

End-to-End Architecture Ownership

• Own the full lifecycle of data architecture, from requirements and conceptual design through implementation, scaling, and ongoing evolution
   
• Define target-state architectures, modernization strategies, and phased delivery roadmaps
• Make architectural trade-offs balancing scalability, performance, reliability, and cost
• Establish data platform standards, design principles, and reference architectures

Data Platform & System Design

• Architect data platforms supporting operational, analytical, batch, and streaming workloads
• Design and govern data modeling strategies across relational and non-relational systems
• Define best practices for schema design, indexing, partitioning, and query optimisation
• Ensure data architectures are resilient, scalable, and production-ready
   

Integration, Processing & Analytics Enablement

• Design robust data ingestion and integration patterns for large-scale systems
• Enable unified or federated access to data across heterogeneous platforms (e.g., query engines such as Trino)
• Support large-scale batch and real-time processing using modern data processing frameworks (e.g., Spark, Flink, Beam)
• Architect reliable messaging and event-driven data flows (e.g., Kafka)
   

Governance, Quality & Security

• Define and enforce data governance, quality, and lifecycle management practices
• Establish standards for metadata, lineage, and observability
• Partner with security and compliance teams to ensure data platforms meet regulatory and privacy requirements
   

Technical Leadership & Delivery

• Act as a principal technical authority, leading architecture reviews and guiding critical design decisions
• Mentor senior engineers and influence best practices across multiple teams
• Collaborate with product, platform, and engineering leadership to align architecture with business priorities.
• Drive execution of complex, multi-team data initiatives from design to production

• 10+ years of experience in data engineering, data platforms, or distributed systems
• Proven track record of planning, delivering, and evolving end-to-end data architectures
• Deep experience working with SQL and NoSQL data systems at scale
• Strong understanding of data integration, processing, and access patterns
• Experience with cloud-native data platforms (e.g., GCP, AWS, Azure)
• Hands-on experience with containerization and orchestration(e.g., Docker, Kubernetes).

Strong communication and documentation skills, with the ability to explain complex architectures clearly

 BE/BS or any relevant degree.

 Travel as required.