Data Architect

JOB PURPOSE

Fastmarkets Data team is a new and dynamic function supporting the business in developing and delivering our data strategy through data, insights, and business intelligence. Highly valued by the business we are working hard to transform data function and ways of working.

Reporting to the Data Platform & AI Manager, we are seeking an experienced Data Architect to shape and guide our data architecture strategy. This is a fantastic opportunity to join us at the start of our journey to bring Data, BI, Analytics, and Insights together into a high performing workstream in its own right.

The Data Architect will play a pivotal role in defining and maintaining the technical architecture of our modern data platform, ensuring scalability, reliability, and optimal performance. Working across decentralized teams and collaborating closely with Business Analysts, this role will be responsible for translating business requirements into detailed technical specifications, defining data quality frameworks, and establishing robust data modeling standards. The Data Architect will act as a technical authority, analyzing and challenging design patterns introduced by federated teams to ensure consistency, best practices, and architectural alignment. The successful candidate will leverage AI tools throughout their workflow and identify opportunities for AI-driven innovation across the data platform.

PRINCIPLE ACCOUNTABILITIES

Technical Requirements & Product Backlog Management

• · Collaborate with Business Analysts to manage and refine the product backlog
• · Translate high-level business requirements into detailed technical specifications and user stories
• · Define technical requirements for data products, pipelines, and platform capabilities
• · Provide technical input on story estimation, complexity, and dependencies
• · Document architectural decisions and create comprehensive technical documentation
• · Work with Senior Data Engineers and MI/Operational reporting teams to refine technical implementation approaches
• · Bridge the gap between business requirements and technical implementation

Data Architecture & Modeling

• · Design and maintain the overall data architecture strategy for the Snowflake-based modern data platform
• · Define and implement data modeling standards, patterns, and best practices
• · Create conceptual, logical, and physical data models that support business requirements
• · Establish and maintain data modeling frameworks (including Data Vault methodology where appropriate)
• · Ensure data models are optimized for performance, scalability, and maintainability
• · Design integration patterns for ingesting data from diverse sources

Data Quality Framework

• · Define comprehensive data quality check definitions and validation rules
• · Establish data quality standards and metrics across the platform
• · Design data quality monitoring and alerting frameworks
• · Work with Data Governance Manager to ensure data quality aligns with governance policies
• · Implement data quality best practices throughout the data lifecycle

Federated Team Governance & Design Pattern Review

• · Analyze and critically evaluate design patterns introduced by decentralized/federated teams
• · Challenge proposed solutions to ensure architectural consistency and adherence to best practices
• · Provide technical guidance and recommendations to distributed teams
• · Balance team autonomy with centralized architectural standards
• · Conduct architecture reviews and design critiques
• · Identify and resolve architectural conflicts or inconsistencies across teams

AI Integration & Innovation

• · Leverage AI tools (e.g., ChatGPT, Claude, GitHub Copilot) throughout daily workflows for documentation, code generation, and problem-solving
• · Identify opportunities to integrate AI capabilities into data architecture and workflows
• · Evaluate and recommend AI-driven solutions for data quality, metadata management, and automation
• · Stay current with AI developments relevant to data architecture and analytics

Platform Optimization & Best Practices

• · Define and enforce best practices for Snowflake, DBT, Dagster, and the broader modern data stack
• · Optimize data platform performance through effective architecture and design
• · Establish standards for code quality, testing, and deployment
• · Ensure alignment with DataOps principles and "data-as-code" approach
• · Continuously evaluate new technologies and tools for potential adoption

KEY INTERFACES

• · Data Platform & AI Manager
• · Business Analysts
• · Head of Cloud Engineering
• · Senior Data Engineer
• · Data Governance Manager
• · Data Operations Engineer
• · BI Engineers and Data Analysts
• · MI and Operational Reporting Teams
• · Federated data teams across business units
• · Business Stakeholders across Fastmarkets