Principal Data Modeller

• 21 Sep 2026
• Permanent / Full Time
• Email: Darragh Gannon

About the role

We are looking for a Principal Data Modeller/Architect to design and own the enterprise data models that power our investment, portfolio, and risk analytics and increasingly, our AI agents and data science workloads. You will work primarily in Snowflake, building dimensional models and semantic layers that make complex wealth management data (positions, holdings, transactions, performance, risk, benchmarks) easy, fast, and trustworthy to query whether the consumer is a BI dashboard, a data scientist's notebook, or an LLM-powered agent.

This is a hands-on, senior individual-contributor role for someone who thinks in facts and dimensions, cares deeply about semantic consistency, and wants to shape how a financial institution's data is structured for the next generation of AI-driven consumption.

About Your experience

• Design, build, and maintain dimensional data models (star and snowflake schemas, conformed dimensions, slowly changing dimensions, fact tables at appropriate grain) across investment and operational domains — portfolios, securities, transactions, positions, performance, risk, and client/account data.
• Design and maintain semantic models / semantic layers on top of the dimensional layer (e.g. via dbt Semantic Layer, Cube, LookML, or Snowflake Semantic Views) that expose consistent, governed business metrics and entities to downstream tools, data science workflows, and AI agents.
• Partner with AI/ML engineering teams to structure data specifically for AI agent consumption — clear entity definitions, well-documented metrics, predictable joins, and metadata/context that make models "agent-readable" (e.g. for text-to-SQL, RAG over structured data, or tool-calling agents).
• Translate business and investment concepts (e.g. NAV, AUM, exposure, attribution, benchmark comparisons) into rigorous, reusable data model constructs in collaboration with portfolio managers, quants, and risk teams.
• Own data modelling standards: naming conventions, grain definitions, surface-level documentation, and a shared business glossary that keeps models consistent across domains and teams.
• Optimize Snowflake schema design for performance and cost — clustering, materialized views, incremental build strategies, and appropriate use of Snowflake features (e.g. dynamic tables, streams, tasks).
• Collaborate with data engineers on dbt (or equivalent) implementations of the models you design, reviewing DAGs, tests, and documentation for correctness and maintainability.
• Support data scientists with model structures suited to feature engineering and analytical workloads, distinct from BI-oriented reporting structures where needed.
• Establish data quality and governance controls (tests, contracts, lineage) so models remain trustworthy as both human and AI consumers scale.
• Mentor junior data modellers/analytics engineers and act as a design-review authority for new data model proposals.

Your skills

• Deep expertise in dimensional modelling theory and practice: fact/dimension design, grain, conformed dimensions, SCD types 1/2, bridge tables, and handling many-to-many relationships.
• Strong SQL skills, with the ability to design for both transactional correctness and analytical performance in a columnar, cloud-native warehouse.
• Solid understanding of semantic modelling concepts: metrics definitions, business glossaries, entity relationships, and how these are exposed consistently to multiple consumption layers.
• Working knowledge of how AI agents and LLMs consume structured data, schema design choices that improve reliability of text-to-SQL, metric consistency, and reduced ambiguity for automated querying.
• Familiarity with data science workflow needs: feature engineering patterns, train/test data hygiene, point-in-time accuracy, and the difference between reporting-oriented and ML-oriented data structures.
• Understanding of data governance practices: documentation standards, data contracts, lineage tools, and data quality testing frameworks (e.g. dbt tests, Great Expectations).
• Ability to communicate model design trade-offs clearly to both technical and business stakeholders, including investment professionals without a data background.
• Comfort with version control and CI/CD practices for analytics code (Git-based workflows, PR review for model changes).

Your day-to-day

• Be the voice of the data model — explain why good data design is crucial to success.
• Work closely with other engineers and stakeholders to understand data requirements, and contribute to delivering practical, scalable solutions.
• Assist in analysing existing systems, including legacy applications, batch jobs, and data flows, and contribute to improving documentation and visibility.
• Support the team in building and maintaining application integrations, data pipelines, and automation workflows.
• Collaborate with engineers, analysts, and business teams to understand requirements and help translate them into reliable, well-structured solutions across applications and data.
• Follow engineering best practices, including documentation, version control, and structured change processes.
• Contribute to improving system reliability through automation, monitoring, and proactive issue resolution.
• Identify opportunities to introduce AI-driven automation in workflows, experiment with AI agents or copilots to improve engineering efficiency, and contribute to AI-enabled solutions across applications and data platforms.

• APPLY NOW