Hands-on analytics engineering
Production dbt delivery, legacy-model refactoring, Snowflake and BigQuery modelling, automated testing, CI/CD, data-quality controls and maintainable analytics architecture.
UK-based · Remote with UK and international teams
Senior analytics engineer for trusted reporting
Hands-on delivery across dbt, Snowflake, BigQuery, Looker and business-critical metrics.
I join data and finance teams to deliver, refactor and stabilise production analytics systems. The work combines more than eight years of analytics engineering experience with practical control over model grain, semantic logic, testing, reconciliation and the reporting decisions downstream.
Contract, interim and fixed-scope support
Work with Gavin
Semantic layers and business reporting
Looker and LookML development, reusable metric logic, dimensional modelling, KPI definitions, source-to-report lineage and dashboard reconciliation.
Reporting Trust diagnostics
Fixed-scope investigation of one contested business metric across finance, CRM, spreadsheets, transformations, dashboards and decision workflows.
Interim and programme support
Senior analytics engineering and reporting support for data-platform, ERP, CRM, finance-transformation and AI-readiness programmes.
Selected delivery evidence
Production work, not just a framework
Reporting Trust is grounded in hands-on analytics engineering delivery. These examples show the kinds of systems, transformations and reporting foundations I have built and improved in production roles.
- Refactored a legacy dbt and Snowflake estate into cleaner modular models with automated testing and reviewed releases.
- Converted nested GA4 events from BigQuery into normalised Snowflake models for consistent Looker reporting.
- Built an eight-source BigQuery and dbt warehouse that replaced fragmented reporting with live dashboards.
The Data Value Chain
Data creates value only when the chain reaches action
Reporting Trust is what stops the chain breaking between technical delivery and business value. With the semantic layer made explicit, the chain is Capture -> Transform [Semantic Layer Gate] -> Interpret -> Act -> Realise. The gate is the control point at the exit of Transform, not a sixth stage.
Record reality
The systems, events, fields, and behaviours that record what is happening.
Structure reality
The models, definitions, joins, semantic rules, reports, dashboards, and recurring packs that make data usable.
Understand reality
The context, caveats, evidence, and judgement needed to explain what the numbers mean.
Decide and respond
The decision, owner, workflow, or operating rhythm where understanding changes behaviour.
Create value
The commercial, operational, or risk outcome the business can connect back to the action.
The Reporting Trust Framework
Connect trusted reporting to realised business value
AI can accelerate the Data Value Chain, but the semantic layer decides whether it accelerates clarity or confusion. Reporting Trust is the condition that lets humans and AI use business numbers safely.
The problem
Trust breaks long before your dashboards do
Numbers disagree
Dashboards, finance packs, spreadsheets, and source systems show different answers for the same business question.
Meaning is missing
The business meaning layer is undocumented, so teams cannot tell which definitions, caveats, or assumptions are safe to reuse.
Decisions slow down
Leadership time shifts from deciding what to do into debating which number is safe enough to use.
AI scales confusion
Automation and AI make the problem faster if definitions, ownership, caveats, and source logic are not trusted first.
Fixed-scope diagnostic
Have one number everyone keeps arguing about?
The Metric Trust Audit traces one contested metric across the Data Value Chain, including its semantic assumptions and AI-readiness risk, then shows where trust breaks and what to fix first.
Supporting resources
Prefer a self-serve route? Start with the book or toolkit
Free Chapter
The opening chapter introduces the reporting trust problem and the first places to inspect.
Get Free ChapterFull Book
The complete field guide for leaders and data teams who need a shared way to restore trust.
Buy the BookBlueprint Toolkit
A separate self-serve digital package for documenting the Semantic Layer Gate, AI-readiness risks, and the practical improvement plan.
Buy the ToolkitAI Workflow Kit
Sixty controlled AI workflows for dbt, SQL, semantic layers, testing, documentation, pull requests and analytics engineering governance.
View the KitFree chapter
See the first part of the method before you buy
Request the free opening chapter by email and get the PDF delivered directly to your inbox. Paid book and toolkit purchases use the separate checkout flow.