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 be responsible for designing the enterprise data architecture underpinning Quantios Insights, our next-generation data platform that unifies data from all Quantios products and powers analytics, regulatory insights, MCP automation, and AI-driven capabilities. You will define the architectural standards, models, integration patterns, and governance frameworks that ensure the platform is reliable, scalable, secure, and adaptable to diverse customer technology stacks.
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 Fabric, Databricks, and Snowflake to support customer deployment flexibility.
- Contribute to the long-term roadmap for data and AI capabilities across Quantios products and internal teams.
- 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 Fabric, Databricks, or Snowflake.
- Ensure semantic models, KPIs, and analytical structures (e.g., Power BI, Fabric semantic models) are consistent, scalable, and aligned with business requirements.
- 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.
- Guide implementation of Purview, Unity Catalog, or Snowflake governance tooling depending on platform.
- 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.
- Collaboration & Technical Leadership
- Work closely with Portfolio Architects to ensure alignment between 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.
- Participate in technical reviews, solution assessments, and architectural governance forums.
- Customer & Partner Enablement
- Define customer deployment reference architectures for Fabric, Databricks, and Snowflake.
- Support customer discussions as a subject matter expert on analytics, AI architectures, and integration of Quantios data into client environments.
- Ensure that Quantios Insights can be deployed flexibly and securely in customer tenancies while maintaining architectural integrity.
- 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 of experience in data engineering, data architecture, or enterprise architecture roles.
- Strong expertise with Microsoft Fabric, Azure Databricks, Snowflake, and modern Lakehouse/data platform architectures.
- Deep experience designing ETL/ELT pipelines, medallion architectures, and semantic models.
- Strong proficiency in SQL, Python, and cloud-based data engineering tooling.
- Experience with metadata and governance tools (Purview, Unity Catalog, Snowflake Governance, Fabric Governance).
- Experience designing data platforms to support analytics, BI, operational reporting, and AI/ML workloads.
- Understanding of LLM/RAG data preparation, unstructured data processing, and vectorisation workflows is advantageous.
- Strong communication skills with the ability to collaborate with engineering, architecture, product, and customer teams.
- Ability to work across cloud environments and support multi-tenant enterprise customer requirements.
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