Senior Analytics Engineer

Eden Scott; Glasgow; Hybrid; GBP 65,000 - 95,000; Full time

Job summary

Eden Scott in Glasgow partners with a market-leading SaaS business to recruit a Senior Analytics Engineer. You will own the semantic layer between raw data and insights, dashboards and AI tools, building scalable, governed data products.

The role focuses on semantic modelling, metrics governance and repeatable data products, with strong SQL, Power BI and automation skills, in a hybrid Glasgow setting.

Qualifications

• Proven experience as an Analytics Engineer or data modelling specialist.
• Strong understanding of semantic modelling, metric layers, star schema and Kimball.
• Advanced SQL skills including query optimisation and stored procedure development.
• Strong Power BI experience including DAX, M Query and dataset modelling.
• Experience turning ad-hoc reporting requirements into governed, reusable solutions.
• Strong understanding of data quality, lineage, governance and documentation.
• Ability to translate complex business requirements into scalable data models.
• Comfort with AI tools and preparing models for Copilot or NL analytics.

Responsibilities

• Design, build and maintain governed semantic models and datasets.
• Create reusable business-ready data models built on Kimball and star-schema principles.
• Ensure data definitions are consistent, trusted and scalable across reporting, analytics and AI use cases.
• Establish and document core business metrics and definitions.
• Own standards around data quality, lineage, documentation and governance.
• Transform recurring requests into self-service, reusable analytical products.
• Reduce reliance on manual reporting and ad-hoc analysis.
• Drive automation of repetitive analytical processes using Power Automate.
• Prepare semantic models for Copilot, AI agents and natural-language querying.
• Create descriptions, metadata, guardrails and business context to improve AI-generated insights.
• Use AI tools to accelerate development, documentation and optimisation while applying rigorous validation and QA.
• Set best practices for analytics engineering and data modelling.
• Mentor Analytics Engineers and support the wider data team.
• Partner closely with Data Engineering, Product and Commercial teams to deliver scalable solutions.

Skills

Semantic modelling

Star schema

Kimball methodology

SQL

Power BI

DAX

M Query

Data governance

Tools

Power Automate

Job description

Senior Analytics Engineer | Competitive Salary | Hybrid – Glasgow

Join a market-leading SaaS business building AI-ready data products and transforming how organisations use data

Are you an experienced Analytics Engineer who thrives on creating scalable, governed data models rather than delivering one-off reports? Do you enjoy turning complex business questions into reusable data products that power analytics, automation and AI?

We are partnering with a high-growth SaaS organisation where data is central to their product strategy. With a strong Data Engineering function already in place, they're looking for a Senior Analytics Engineer to own the semantic layer that sits between raw data and the insights, dashboards and AI tools relied upon across the business.

What You'll Be Doing

Own the Semantic Layer

• Design, build and maintain governed semantic models and datasets


• Create reusable business-ready data models built on Kimball and star-schema principles


• Ensure data definitions are consistent, trusted and scalable across reporting, analytics and AI use cases

Define Metrics & Governance

• Establish and document core business metrics and definitions


• Own standards around data quality, lineage, documentation and governance


• Ensure reports, dashboards and AI tools return consistent answers from trusted data sources

Build Repeatable Data Products

• Transform recurring requests into self-service, reusable analytical products


• Reduce reliance on manual reporting and ad-hoc analysis


• Drive automation of repetitive analytical processes using tools such as Power Automate


• Prepare semantic models for Copilot, AI agents and natural-language querying


• Create descriptions, metadata, guardrails and business context that improve AI-generated insights


• Use AI tools to accelerate development, documentation and optimisation while applying rigorous validation and quality control

Provide Technical Leadership

• Set best practices for analytics engineering and data modelling


• Mentor Analytics Engineers and support the wider data team


• Partner closely with Data Engineering, Product and Commercial teams to deliver scalable solutions

What We're Looking For

• Proven experience as an Analytics Engineer, BI Developer or Data Modelling specialist


• Strong understanding of semantic modelling, metric layers, star schema and Kimball methodology


• Advanced SQL skills including query optimisation and stored procedure development


• Strong Power BI experience including DAX, M Query and dataset modelling


• Experience turning ad-hoc reporting requirements into governed, reusable solutions


• Strong understanding of data quality, lineage, governance and documentation


• Experience identifying and automating manual analytical processes


• Confident use of AI tools with the ability to validate outputs and ensure production readiness


• Ability to translate complex business requirements into scalable data models


• Experience preparing data platforms for Copilot, AI agents or natural-language analytics


• Previous experience with procurement, spend, supplier or other complex master-data domains


• Experience mentoring or leading Analytics Engineers


• Familiarity with Power Automate and workflow automation tools


• Play a pivotal role in shaping the organisation's AI and analytics capabilities


• Move the business from reactive reporting to productised, self-service analytics


• Own the semantic layer that powers reporting, dashboards and AI-driven insights


• Work alongside a highly capable Data Engineering team


• Join a culture that values innovation, autonomy, ownership and continuous improvement


• Have genuine influence on data strategy and technical standards across the business


• Enjoy building systems, standards and reusable solutions rather than one-off reports


• Think in terms of data products, models, metrics and governance


• Challenge assumptions and validate outputs, especially when AI is involved


• Continuously seek opportunities to automate and improve analytical processes


• Can bridge the gap between technical teams and business stakeholders


• Take ownership and make sound technical decisions with confidence