Solution Architect – Data Platform

Responsibilities

• Own enterprise-level logical and physical data architecture design
• Define and govern data modelling standards aligned with enterprise principles
• Develop scalable architecture patterns for data ingestion, transformation, and serving layers
• Lead and define migration strategies from legacy platforms to modern big data environments
• Conduct architecture reviews and performance optimisation assessments
• Establish performance tuning and scalability frameworks
• Develop reusable design patterns and reference architectures
• Ensure compliance with data governance, security, and regulatory requirements
• Provide solution sizing, estimation inputs, and risk assessments
• Support production issue resolution at architectural level (Level 3)
• Mentor senior engineers and guide architectural best practices
• Define target-state data architecture and standards
• Translate business strategy into scalable data solutions
• Work across business, engineering, and governance teams
• Identify architectural and migration risks early
• Drive adoption of standards and design patterns
• Provide oversight across multiple squads or workstreams
• Elevate engineering maturity and reduce technical debt

Requirements

Experience & Qualifications

• Bachelors degree in Computer Science, Information Systems, Engineering, or related discipline
• 10+ years of experience in data engineering, data architecture, or platform architecture roles
• Demonstrated experience designing enterprise-scale data platforms
• Strong experience in distributed data processing technologies
• 8+ years of hands-on experience in enterprise data modelling

Data Platform & Architecture

• Experience designing modern data platforms (Lakehouse, Data Lake, Data Warehouse, Hybrid architectures)
• Strong understanding of distributed data processing frameworks (e.g., Spark-based platforms)
• Experience with enterprise-grade relational databases (Oracle, SQL Server, PostgreSQL, etc.)
• Knowledge of both cloud and on-premise big data environments

Data Modelling

• Conceptual, Logical, and Physical modelling
• Dimensional modelling (Kimball)
• Data Vault methodologies
• Canonical data models
• Alignment with Enterprise Information Model (EIM)
• Metadata management and schema governance practices

Migration & Modernisation

• Legacy RDBMS to modern data platforms
• On-premise to cloud/hybrid transformation
• Batch to near real-time processing
• Data refactoring and technical debt remediation
• Cutover strategy, rollback planning, and risk mitigation
• Data reconciliation and validation frameworks post-migration

Performance Engineering

• Distributed compute optimisation (partitioning, indexing, clustering strategies)
• Query performance tuning and workload management
• Storage optimisation and cost-performance balancing
• Data pipeline performance monitoring and troubleshooting
• Scalabilty planning and capacity modelling

Governance & Engineering Practices

• Data governance frameworks (cataloguing, lineage, access control)
• CI/CD design for data pipelines
• Infrastructure as Code and automation concepts
• Data quality frameworks and observability practices
• Experience operating in Agile delivery environments