Job DescriptionThis is a technical leadership role rather than a hands-on development role: the architect owns the technical integrity of the solution and leads, reviews and challenges multidisciplinary engineering teams, without personally developing applications, ETL pipelines or platform components.
Responsibilities
- * Own and maintain the end-to-end solution architecture across application, data, integration, cloud, security and analytics domains.
- Translate business, functional and non-functional requirements into coherent and implementable technical solutions.
- Define architecture principles, patterns, interfaces and major technical decisions, and record them.
- Lead and provide technical direction to multidisciplinary engineering and delivery teams.
- Coordinate specialist architects and technical leads where available, and work directly with engineers where required.
- Review solution designs and implementation approaches and ensure alignment with the approved architecture.
- Lead the implementation of an overarching Quality Management approach covering the end-to-end solution lifecycle — architecture, data, applications, integrations, infrastructure, security, testing, deployment and operations.
- Establish quality standards, controls, acceptance criteria, quality gates, review mechanisms and automated testing practices to ensure solution reliability, consistency and maintainability.
- Govern and continuously assess solution quality throughout delivery — monitoring quality performance, identifying defects and systemic quality risks, driving root-cause analysis and corrective action, and promoting continuous improvement.
- Lead resolution of complex technical issues and cross-domain dependencies.
- Ensure security, privacy, data governance, quality, performance, scalability, availability and maintainability requirements are addressed.
- Ensure appropriate DevOps, automation, testing, monitoring and engineering-quality practices are incorporated into delivery.
- Assess technical risks, technical debt and solution/release readiness.
- Work closely with Project Managers on technical estimation, planning, sequencing, dependencies, risks, progress and delivery readiness.
- Provide clear technical reporting, and communicate complex architecture matters to management and non-technical stakeholders.
QualificationsRequired: Bachelor’s degree in computer science, Software Engineering, Information Systems, Computer Engineering or a related technical discipline.
Required: 12 + years in technology roles, of which at least 5 in Solution Architecture, Technical Architecture or comparable technical leadership, with the most recent role held in an architecture or technical leadership capacity.
Required: Proven delivery of complex enterprise solutions combining data platforms and applications, and experience leading multidisciplinary technical teams across the full solution delivery lifecycle.
Required: Personal authorship of core architecture artifacts — solution and interface designs, integration designs, non-functional requirements, security designs and architecture decision records.
- End-to-End Solution Architecture: integrated solutions spanning source systems, ingestion, processing, governance, semantic layers, analytics, APIs and web applications.
- Data Platform Architecture: ETL/ELT, data integration, transformation, curation, modelling, data quality, metadata, cataloguing, lineage, governance, MDM/RDM and semantic layers.
- Application & Integration Architecture: modern web architectures, APIs, API gateways, microservices, containers, Kubernetes and distributed application patterns.
- Cloud & Platform Architecture: cloud and hybrid architectures, scalability, resilience, networking, compute, storage, observability and platform integration.
- Security & Privacy Architecture: IAM, RBAC/ABAC, API security, encryption, masking, tokenization, data privacy, row/column-level security and secure development practices.
- Analytics & AI/ML: descriptive, diagnostic, predictive and prescriptive analytics, semantic modelling and machine-learning concepts.
- DevOps & Quality Engineering: Git, CI/CD, release management, deployment automation, and automated quality practices including unit, integration, regression, performance, SAST and DAST testing.
- Agile Technical Delivery: Scrum, Kanban, backlog refinement, technical decomposition, dependency management, technical debt and release planning.
