Data Design Lead (Snowflake)

The role owns the technical integrity, evolution, and enablement of the ARCUS Snowflake platform, ensuring it is used optimally, consistently, and safely across the organisation. This includes acting as the primary escalation point, design authority, and internal expert for Snowflake architecture, DBT-based transformation patterns, and medallion-layer design. 

Key Responsibilities: 

1. Solution Design & Low-Level Architecture 

• Lead detailed data solution design for small to medium initiatives and complex change within existing products. 

• Translate conceptual and logical data models into physical Snowflake and DBT implementations aligned to ARCUS standards. 

• Define and review detailed designs across: 

• Data ingestion and replication (including CDC patterns) 

• Transformation and orchestration 

• Schema evolution and versioning 

• Data lineage, observability, and quality controls 

• Integration patterns between operational systems and analytical products 

• Act as an approver for low-level data designs, ensuring adherence to architectural principles, security requirements, and platform standards. 

• Provide hands-on architectural guidance to engineering teams during delivery, particularly in complex or ambiguous problem spaces. 

2. Operational Architecture & Production Stewardship 

• Own the run-state architectural integrity of the ARCUS platform across bronze, silver, and gold layers. 

• Partner with platform and product teams to support production data solutions, balancing stability with ongoing evolution. 

• Lead architectural triage for: 

• Data quality incidents 

• Pipeline failures and performance degradation 

• Structural or modelling issues affecting downstream consumers 

• Maintain and prioritise architecture-led technical backlogs covering refactoring, resilience, cost optimisation, and platform hygiene. 

• Ensure operational patterns (monitoring, alerting, error handling, recovery) are applied consistently across solutions. 

• Own Snowflake run-state health, including warehouse sizing strategies, query performance, cost efficiency, and concurrency management. 

• Lead architectural decisions related to Snowflake cost optimisation, including workload isolation, warehouse reuse, and consumption controls. 

• Define and enforce Snowflake-native observability patterns (query history, resource monitoring, failure diagnostics). 

3. Snowflake Platform Evolution & Enablement 

• Act as TMHCC International’s internal Snowflake Subject Matter Expert (SME), providing authoritative guidance on platform capabilities, limitations, and best-fit usage. 

• Serve as the design and escalation authority for Snowflake-specific decisions, including performance optimisation, warehouse strategy, data sharing, security models, and cost governance. 

• Partner with Snowflake (and strategic vendors where appropriate) to stay aligned with roadmap, best practices, and reference architectures. 

• Proactively educate and upskill engineering and analytics teams on effective Snowflake usage through patterns, examples, and hands-on guidance. 

• Guard against anti-patterns and misuse of Snowflake by enforcing opinionated, well-documented architectural guardrails. 

• Own and evolve canonical Snowflake + DBT medallion patterns (bronze, silver, gold), including layer responsibilities, modelling conventions, and performance expectations. 

• Act as final design authority for DBT project structures, model materialisation strategies, testing conventions, and semantic-layer design. 

• Represent TMHCC International as the Snowflake architectural point of contact for platform discussions, roadmap alignment, and technical escalations.  

4. Standards, Governance & Enterprise Alignment 

• Define and maintain data architecture standards, including: 

• Naming conventions 

• Semantic and analytical modelling guidelines 

• Layering and ownership conventions within the medallion architecture 

• Actively contribute to data governance activities, including: 

• Business glossary alignment 

• Data lineage and impact analysis 

• Reference and master data alignment across domains 

• Represent Data Design & Engineering in enterprise design forums, architecture boards, and technical working groups. 

• Ensure that ARCUS aligns with broader TMHCC International technology and information architecture strategies. 

5. Leadership, Mentorship & Delegated Authority 

• Act as deputy for the Head of Data Design & Engineering, providing continuity of leadership and decision-making when required. 

• Mentor data engineers and analysts, raising overall architectural maturity and design capability. 

• Lead targeted architecture initiatives or “architecture sprints” to unblock delivery teams or advance strategic platform objectives. 

• Provide clear, authoritative technical direction while fostering autonomy and accountability within delivery teams. 

• Influence cross-functional stakeholders through strong architectural reasoning rather than positional authority. 

Performance Objectives:   

• Architectural Consistency: Establish and enforce consistent low-level data architecture patterns across the ARCUS platform, ensuring all new and materially changed solutions align with agreed Snowflake, DBT, and medallion-layer standards with minimal rework. 

• Production Stability & Operability: Improve the stability, data quality, and operability of production data solutions by strengthening run-state architectural patterns, reducing repeat incidents, and embedding robust observability and error-handling approaches. 

• Deputy Effectiveness: Act as an effective deputy to the Head of Data Design & Engineering by owning day-to-day architectural decision-making and operational design oversight, materially reducing escalation and dependency on the role holder. 

• Platform Evolution & Governance: Drive the controlled, value-led evolution of the Snowflake platform by maintaining reference architectures and standards and enabling the pragmatic adoption of new capabilities without increasing cost or operational risk. 

• Architectural Maturity Uplift: Uplift the architectural capability and autonomy of engineering teams through mentoring, design reviews, and clear guidance, resulting in higher-quality designs and reduced reliance on central architecture. 

Skills and Experience Specification: 

Essential: 

• Significant experience in data architecture, data solution architecture, or senior data engineering roles. 

• Experience acting as a Snowflake technical lead, platform owner, or internal SME within a medium-to-large organisation. 

• Familiarity with Snowflake roadmap-driven adoption and controlled enablement in regulated environments. 

• Deep, hands-on expertise with Snowflake as an enterprise analytics platform, including performance tuning, cost management, access control, and production operations, alongside advanced SQL and DBT. 

• Proven experience designing and governing enterprise-scale data platforms. 

• Experience with ingestion and CDC tooling (e.g. Fivetran or equivalent). 

• Strong data modelling capability across conceptual, logical, and physical layers. 

• Familiarity with GDPR and implementation considerations for enterprise data platforms. 

• Ability to produce clear, pragmatic architecture documentation, patterns, and standards. 

• Excellent collaboration and stakeholder management skills, including the ability to challenge constructively. 

Desirable: 

• Experience in commercial or specialty insurance data domains. 

• Understanding of operational and transactional systems (e.g. Pega) and their integration with analytical platforms. 

• Experience operating in regulated or risk-sensitive environments. 

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