Quantios
Data Architect (UK)
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Data Architect
Quantios is a leading provider of software solutions for the trust administration and corporate services industry. With over 30 years of experience, we empower our clients with innovative technology that enhances governance, operations, and investment on a global scale. At Quantios, we are committed to fostering a diverse and inclusive workplace where creativity, learning, and collaboration drive success.
As a Data Architect at Quantios, you will own the architecture of Quantios Insights, our multi-tenant analytics platform. Built on Azure Databricks and Unity Catalog, Insights unifies data from all Quantios products and powers analytics, regulatory insights, MCP-based data access, and AI-driven capabilities for regulated financial services customers.
You will define the architectural standards, models, integration patterns, and governance frameworks that keep the platform reliable, scalable, secure, and demonstrably isolated between customers. This is a hands-on role: you will set the standard through working code and documented patterns, using SQL, Python, and dbt in your daily work.
This role also plays a central part in the internal AI Enablement Centre of Excellence, ensuring high-quality datasets flow into RAG pipelines, LLM evaluation frameworks, and strategic AI initiatives across the business. You will collaborate closely with Data Engineers, LLMOps Engineers, Product Owners, Portfolio Architects, and engineering teams to shape Quantios’ data and AI ecosystem.
Job Responsibilities
Enterprise Data Architecture & Strategy
- Define the architectural vision, principles, and standards for Quantios’ enterprise data platform aligned to the business and product strategy.
- Own and evolve the data architecture for Quantios Insights, including data ingestion patterns, lakehouse design, semantic modelling, and analytical layers.
- Define architectural guardrails and reference patterns for Databricks to support customer deployment flexibility.
- Contribute to the long-term roadmap for data and AI capabilities across Quantios products and internal teams.
Hands-On Delivery & Technical Leadership
- Use SQL, Python, and dbt hands-on: build reference implementations, review and write pull requests, and unblock Data Engineers directly.
- Set the standard through working code and documented patterns (architecture decision records, runbooks, reference models), not only diagrams.
- Review and challenge work from engineering teams and external delivery partners, confirming that what is documented matches what is built and running.
- Work closely with Portfolio Architects to align product architecture, data architecture, and platform strategy.
- Collaborate with Product Owners to translate analytical and AI requirements into data architectural designs.
- Provide architectural oversight to Data Engineers, guiding implementation consistency and quality, and participate in technical reviews and architectural governance forums.
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Data Modelling & Lakehouse Design
- Lead the design of domain-driven canonical data models for all Quantios products and cross-product datasets.
- Architect medallion/lakehouse models (bronze, silver, gold) using Databricks.
- Ensure semantic models, KPIs, and analytical structures are consistent, scalable, and aligned with business requirements, and serve whichever BI and application layers consume them.
- Guide Data Engineers in applying architectural patterns and modelling best practices.
Integration Architecture & Ingestion Patterns
- Design data ingestion frameworks for distributed Quantios products using batch, streaming, event-driven, or API-based patterns.
- Define common integration templates for customer and partner ecosystems.
- Support ingestion of structured, semi-structured, unstructured, and log/event telemetry.
- Ensure data flows are resilient, observable, and architected for reliability and cost efficiency.
Governance, Security & Compliance
- Define data governance frameworks covering metadata, lineage, data catalogue, access management, and quality standards, implemented through Unity Catalog.
- Ensure that data architecture adheres to Quantios’ security, privacy, and regulatory requirements.
- Define patterns for sensitive data handling, encryption, and secure data access.
AI & Advanced Analytics Enablement
- Architect dataset structures to support AI, including RAG pipelines, vector search, LLM training/evaluation sets, and semantic enrichment layers.
- Collaborate with LLMOps Engineers to ensure high-quality, well-structured data flows into embeddings pipelines and retrieval systems.
- Support MCP-based data access patterns by designing structured data interfaces and schemas tailored for agent use.
- Define patterns for unstructured data processing and preparation for AI workloads.
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Customer & Partner Enablement
- Define customer deployment reference architectures for Databricks.
- Support customer discussions as a subject matter expert on analytics, AI architectures, and integration of Quantios data into client environments.
Continuous Improvement & Innovation
- Stay current with emerging trends in data engineering, AI data management, lakehouse technologies, governance, and observability.
- Evaluate new tools, patterns, and technologies that enhance the data and AI platform capabilities.
- Promote a culture of continuous improvement within the data engineering and broader architecture teams.
Job Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Information Systems, or a related field; or equivalent experience.
- 7+ years in data engineering, data architecture, or platform architecture, with at least 3 years hands-on on Azure Databricks in production.
- Demonstrable production experience with Unity Catalog, including catalogs and schemas as isolation boundaries, grants, row filters, column masks, service principals, lineage, and system tables. Experience designing or operating a multi-tenant data platform is strongly preferred.
- Hands-on experience implementing fine-grained access control (ABAC or equivalent) on Databricks or a comparable platform.
- Strong working experience with dbt (ideally dbt-databricks), including incremental models, testing, and multi-environment deployment.
- Experience with Terraform (including the Databricks provider) and Databricks Asset Bundles, or equivalent infrastructure-as-code and workload deployment tooling, within CI/CD pipelines (Azure DevOps preferred).
- Strong SQL and Python, used routinely in your current work.
- Deep experience with medallion architecture, dimensional modelling, and semantic layer design.
- Experience designing platforms for regulated environments, with working knowledge of data residency, encryption, private networking, and audit requirements.
- Strong communication skills, with the ability to explain architectural trade-offs to engineers, product owners, and customer security teams.
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