- Technical Architecture Design - Own the technical runbook and define end-to-end technical designs for data solutions across ingestion, transformation, storage, integration, consumption and operational support.
- Platform Pattern Definition - Create and evolve reusable technical patterns, reference architectures and design guardrails for data pipelines, lakehouse or warehouse design, APIs, events and analytical services.
- Engineering Collaboration - Work with data engineers, platform engineers and software engineers to ensure designs are practical, buildable, testable and operationally robust.
- Security and Privacy by Design - Embed appropriate access controls, encryption, masking, segregation, retention, monitoring and audit requirements into technical designs.
- Data Quality and Observability - Define technical implementation patterns for validation, reconciliation, lineage capture, monitoring, alerting and incident investigation.
- Performance and Scalability - Assess and optimise technical designs for latency, throughput, availability, cost, resilience and maintainability.
- Integration and Interoperability - Ensure data solutions integrate effectively with source systems, domain services, data products, catalogues, governance tooling and consumption channels.
- Design Assurance - Review detailed designs, code-level patterns and delivery outputs for alignment with architecture standards and accepted technology choices.
- Technical Debt Management - Identify, document and prioritise remediation of architecture debt, platform constraints and implementation risks.
- Technology Leadership - Maintain strong awareness of relevant data technologies, engineering practices and vendor capabilities, advising teams on appropriate adoption
What does success look like?
- Fully documented data runbook detailing technical patterns across all technologies adopted within the data platform, with a practical maintenance and evolution plan
- Design patterns are consistently adopted by engineering to efficiently deliver solutions aligned with architecture standards.
- Data solutions are secure, reliable and scalable. Security, privacy, data quality and observability are built into designs, with performance, resilience and cost considered from the outset.
Your knowledge and experience
- Strong practical architecture experience with a modern cloud data platform, advanced SQL and engineering-led data delivery; candidates should be able to review designs at pipeline, schema and integration level.
- OLAP & OLTP architecture experience - Snowflake and Oracle (or equivalent)
- Data architecture and data engineering patterns across batch, event-driven and analytical workloads
- Cloud data platform, warehouse and lakehouse architecture
- Advanced SQL and strong understanding of ELT/data transformation patterns
- Data integration, orchestration, APIs and event-driven architecture
- Logical, physical and semantic data design
- Data product schemas, interfaces and data contract design
- Metadata, data catalogue and lineage implementation patterns
- Data security engineering, privacy, identity and access-control patterns
- Performance, scalability, workload and cost optimisation
- DevOps/DataOps practices, version control, CI/CD and infrastructure-as-code awareness
- Testing, reconciliation and automation patterns
- Technology evaluation, architecture decision records and technical documentation
The type of candidate that we’re looking for:
We’re looking for a Data Architect who can combine strong technical knowledge with a practical, collaborative approach. You’ll have experience designing and delivering modern data solutions and be comfortable turning broad architecture principles into clear, workable approaches that engineering teams can use. You’ll understand how data moves through platforms, how systems integrate, and what good looks like when it comes to security, reliability, scalability and performance.
You’ll enjoy working closely with engineers, analysts and other technology teams, helping to shape standards and patterns that make delivery simpler and more consistent. Experience with modern cloud data platforms, data warehousing, SQL, integration and data modelling will be important, and familiarity with technologies such as Snowflake and Oracle would be beneficial. Most importantly, you’ll be someone who can balance technical depth with pragmatism and communicate your thinking clearly to a range of stakeholders.
Critical Skills
• Data Literacy
• Systems Thinking
• Storytelling
• Team Working
• Improvement Mindset
• Digital Effectiveness (incl. AI)
Should you choose to use AI tools to support your application, we ask that you do this thoughtfully. We encourage you to ensure your application reflects your own voice, experience, and motivations. We value authenticity and want to understand your individual strengths and perspectives.
At Baillie Gifford, we are committed to fostering an inclusive and respectful culture in which each of our colleagues can thrive and develop. We believe that our clients are best served by a diverse workforce with the experiences, ideas and perspectives that this brings.
If you are currently working at Baillie Gifford as an employee or contractor please apply to this job from the firm's Workday internal career site.
