Senior Data Engineer

Experis Scotland; Glasgow; On-site; GBP 85,000 - 110,000; Full time

Job summary

Experis Scotland is seeking a Senior Data Engineer to design and scale Snowflake-based data products powering portfolio suitability, risk and wealth analytics. You will build robust data models, ELT pipelines with dbt, and establish governance for AI-ready data foundations.

You will collaborate with business and AI teams to drive adoption of AI-enabled data engineering practices and deliver trusted data for decision making.

Qualifications

• Extensive Snowflake, dbt, SQL and Azure experience.
• Experience building enterprise-scale data platforms and products.
• Advanced dimensional modeling and star schema design.
• Strong governance, metadata, lineage and data quality knowledge.
• Hands-on with GitHub, CI/CD and modern engineering practices.
• Financial Services/Wealth/Asset management background preferred.
• Familiarity with market data providers like Bloomberg/FTSE.

Responsibilities

• Design and deliver scalable data products supporting portfolio suitability and risk analytics.
• Build and optimise Snowflake data models across portfolios, holdings, transactions and market data.
• Create analytics-ready datasets for reporting tools, data analysts and AI apps.
• Engineer data products for future AI initiatives including copilots and decision-support tools.
• Develop and maintain ELT pipelines using dbt and modern software practices.
• Ensure governance, lineage, data quality and security across assets.
• Collaborate with business, analytics and AI teams to accelerate decision making.
• Promote AI-assisted engineering using GitHub Copilot and Claude Code.

Skills

Snowflake

dbt

SQL

Azure

Data modeling

Data governance

GitHub

CI/CD

Portfolio data

AI tooling

Tools

GitHub

CI/CD

Bloomberg

FTSE

Claude Code

Copilot tools

Job description

We're looking for a Senior Data Engineer to build and scale Snowflake-based data products that power portfolio suitability, investment risk, market data, advanced analytics and AI-driven decision making.

This role sits at the intersection of modern data engineering and AI enablement. You'll help create the trusted, governed data foundations needed for reporting, analytics, machine learning, copilots and future agentic AI solutions. The focus isn't just on moving data, it's about making data discoverable, reusable and ready for both human and AI consumers.

What You'll Be Doing

• Design and deliver scalable data products supporting portfolio suitability, investment risk and wealth management analytics.
• Build and optimise Snowflake data models covering portfolios, holdings, transactions, valuations, market data and suitability outcomes.
• Create analytics-ready datasets that can be consumed consistently by reporting tools, data analysts and AI applications.
• Engineer trusted data products that support future AI initiatives, including copilots, AI assistants, knowledge discovery and automated decision-support tools.
• Develop and maintain ELT pipelines using dbt and modern software engineering practices.
• Ensure strong governance, lineage, data quality and security standards across all data assets.
• Work closely with business, analytics and AI teams to identify opportunities where data products can accelerate decision making and productivity.
• Help drive adoption of AI-assisted engineering practices using tools such as GitHub Copilot and Claude Code.

AI & Data Product Focus

AI is a core part of this role. You'll be expected to think beyond traditional data warehousing and build data products that are AI-ready from day one.

This includes:

• Designing datasets that can be easily discovered, understood and consumed by AI tools and large language models.
• Creating high-quality, well-governed data foundations for machine learning, generative AI and agentic workflows.
• Leveraging AI-assisted development tools to improve engineering productivity, code quality and delivery speed.
• Supporting the development of AI-powered analytics, copilots and decision-support capabilities across the business.
• Ensuring AI use cases are built on trusted, auditable and secure data.

Key Skills & Experience

• Strong Snowflake, dbt, SQL and Azure experience.
• Experience building enterprise-scale data platforms and data products.
• Advanced dimensional modelling and Star Schema design.
• Strong understanding of data governance, quality, metadata and lineage.
• Hands-on experience with GitHub, CI/CD and modern engineering practices.
• Financial Services, Wealth Management or Asset Management experience.
• Strong knowledge of portfolio, holdings, transactions and market data.
• Experience working with Bloomberg, FTSE or similar market data providers.
• Comfortable using AI-assisted engineering tools such as GitHub Copilot and Claude Code.

Ideal Candidate

An experienced Data Engineer with deep Snowflake expertise and a strong Financial Services background who is excited about the next generation of AI-enabled data platforms. You'll understand how to build robust, governed data products that support traditional reporting and analytics today while laying the foundations for copilots, AI assistants and agentic AI capabilities in the future.