DATA ENGINEERING
Build the foundation your data depends on.
Robust data pipelines and architectures that provide clean, trusted and scalable data.
Good analytics starts long before the dashboard.
I engineer the systems that move, transform, structure and validate your data so the information reaching your users can actually be trusted.
What I deliver
Data pipeline development
Reliable pipelines that move data from source systems into the environments where it can be used.
Data modeling
Structured models designed around the way your business needs to consume information.
Data lake & warehouse design
Scalable data architectures designed to support reporting, analytics and future growth.
Data quality & controls
Processes, validation and controls that improve reliability, consistency and confidence in your data.
Data Integration
Connect systems and datasets that currently operate in isolation.
Data Transformation
Turn raw operational data into structured, meaningful information ready for analysis.
Productised Expertise
Is your data actually trustworthy?
Data Trust Assessment
A structured investigation into the reliability of your data environment.
I'll trace data from source through transformation and reporting to identify where trust is being lost.
The assessment examines:
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Completeness — whether expected data is actually present
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Accuracy & validity — whether data conforms to expected ruless
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Consistency — whether information agrees across systems
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Reconciliation — whether source data survives correctly downstream
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Freshness — whether data arrives when the business expects it
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Traceability & controls — whether problems can be understood and detected
The assessment may uncover missing or duplicate records, inconsistent data, broken or fragile pipelines, incorrect transformations, reporting discrepancies, manual intervention points, unclear ownership and gaps in monitoring or controls.
What you get
Data Trust Scorecard
A structured view of how your critical data flow performs across the key dimensions of data trust, highlighting areas of strength, concern and significant risk.
Critical Data Flow Map
A clear view of how your critical data moves from source through pipelines, transformations and storage to the systems, reports or people that ultimately depend on it.
Data Profiling & Reconciliation Evidence
Where applicable, critical datasets are profiled and compared across systems to provide evidence of missing, duplicate, inconsistent or unexpected data.
Data Trust Rules Register
The important data and business rules identified during the assessment are documented, together with the checks used to determine whether those expectations are being met.
Findings & Risk Register
Evidence-backed findings are documented and prioritised according to severity, business impact and the risk they create.
Improvement Roadmap
A practical, prioritised roadmap showing what should be addressed first, where quick wins exist and which longer-term improvements will strengthen trust in your data.
You leave with: a clear Data Trust Score, evidence-backed findings, a prioritised view of your data risks, and a practical roadmap showing what to fix first.