About the Role :

We're hiring a hands-on Data Architect to own the end-to-end design of our data platform - from source system integration and CDC, through the data lake and Snowflake warehouse, into dimensional and semantic models, the BI layer, and our emerging AI products. This is a senior, high-autonomy role for someone who has architected and scaled modern data platforms before, who still codes and reviews architecture hands-on rather than only diagramming it, and who is comfortable using AI-assisted development tools to move faster without cutting corners on quality or governance.

What You'll Do :

Architecture & Platform Design :

- Design and own the end-to-end data architecture across AWS (S3, IAM, and core data services) and Snowflake - from raw/bronze through conformed/silver to curated/gold zones - partnering with our CloudOps/DevOps team on networking, provisioning, and infrastructure operations.

- Define the standards and patterns for data integration, pipeline orchestration (Airflow/MWAA), and data warehousing that the wider engineering team builds against.

- Evaluate and select tools, patterns, and vendors as the platform scales, balancing cost, performance, and maintainability.

Data Modelling & Warehousing :

- Own conceptual, logical, and physical data modelling across core business domains - using Data Vault for the historised, audit-ready layer and dimensional (star schema) models for consumption.

- Define conformed dimensions, data contracts, and reusable data products consumed by BI, analytics, data science, and AI teams.

Data Integration & Pipelines :

- Architect reliable batch and CDC-based data integration pipelines from core operational systems into the lake and warehouse.

- Partner with data engineers on pipeline reliability, automated testing, schema-drift handling, and SLA design.

BI & Semantic Layer :

- Design the semantic layer and certified metric definitions that power Power BI and self-serve analytics - one trusted definition per business metric, everywhere it's used.

- Guide BI developers and analytics engineers on performant, well-governed dataset design.

AI Enablement :

- Architect the data foundations - governed marts, semantic layer, retrieval/vector infrastructure - that ground the organisation's AI products, including a business-facing GenAI chatbot.

- Partner with data science and AI engineering on feature pipelines, model-serving data needs, and MLOps/LLMOps integration points.

AI-Augmented Engineering :

- Actively use AI development tools (e.g., GitHub Copilot, Cursor, Claude Code, or similar) to accelerate design, coding, testing, documentation, and repetitive engineering work.

- Champion practical AI-assisted workflows across the data organisation - code generation, pipeline scaffolding, test generation, documentation, code review - while holding the line on engineering rigor.

Leadership & Governance :

- Set technical direction and review architecture and design decisions across data engineering, analytics engineering, and BI.

- Partner with the Director of Data on governance and data-quality standards and the technical roadmap; mentor senior engineers.

What You'll Need :

- 12 - 17 years of overall data engineering/architecture experience, including demonstrable experience architecting production systems - not only diagramming them. You're expected to own AWS architecture and design decisions, not perform hands-on networking work yourself - our CloudOps/DevOps team implements and operates that layer.

- Expert-level experience with AWS (S3, IAM, and core data services) and Snowflake (architecture, performance tuning, cost optimisation, RBAC).

- Hands-on experience across the full data lifecycle : data integration/CDC, data pipeline engineering, data warehousing, dimensional modelling (Kimball) and/or Data Vault, semantic/BI layer design, and exposure to AI/ML-driven data products.

- Expert-level SQL, Python, and PySpark - able to write, review, and debug production code, not just specify it.

- Practical, hands-on experience using AI development tools (e.g., GitHub Copilot, Cursor, Claude Code, ChatGPT/LLM-based tooling) for coding, automation, documentation, and other development workflows, with a clear point of view on where they help and where they don't.

- Experience with workflow orchestration (Airflow or equivalent) and modern transformation tooling (dbt or equivalent).

- Strong communication skills - able to work directly with business stakeholders and translate ambiguous requirements into resilient architecture.

Nice to Have :

- Experience in financial services, lending, collections, or debt-settlement/consumer-finance environments, including awareness of relevant regulatory considerations (e.g., FDCPA, TCPA, CFPB-adjacent data handling).

- Experience standing up or scaling a data organisation from a small team, including defining architecture standards from scratch.

- Experience with GenAI/RAG architectures, vector search, and LLMOps tooling.

- Relevant certifications (AWS, SnowPro).