Data depth and the hard problems beneath it are what hold my attention.
I help organisations understand what their data can genuinely support — then engineer the data products, analytical models and BI needed to make that understanding dependable.
Data volume is not the same as data usefulness. The decisive questions are structural: what exists, what one record represents, how often it changes, how sources relate, and where the evidence stops being sufficient.
I focus on the difficult part of data: understanding its structure well enough to make a sound decision about what should be built. Grain matters especially — because the level at which data is recorded determines which questions can be answered reliably.
The practical questionWhat can this data answer today — and what would have to change to make the unanswered questions feasible?
Cloud services, Spark, SQL, BI and ML are useful when they solve a defined problem. I prefer standards and explicit interfaces where they preserve ownership, portability and engineering control.
I work across the data lifecycle, but the purpose stays narrow: establish what is true, engineer what is needed, and make the resulting information useful and trustworthy.
Assess the data estate and the path from source to decision. Determine what the organisation can reliably know, what it cannot yet know, and what would make the gap feasible to close.
Design and implement the ingestion, transformation and storage patterns needed to make trusted data repeatable, observable and maintainable.
Build analytical models and BI experiences that preserve business meaning, expose assumptions and give stakeholders a consistent basis for action.
Before recommending a platform, dashboard or ML product, I examine the evidence underneath the request. Data is important; grain is what determines the answer.
The assessment connects technical reality to the questions the organisation wants to answer. It identifies useful evidence, ambiguity, fragmentation and the changes required to move from “cannot answer” to “can answer reliably.”
From authenticated source access through incremental extraction, layered refinement, governed storage and role-secured consumption. The architecture is evidence of the design principle: vendors provide capabilities; the organisation retains the logic, boundaries and decisions.
Business transformations remain expressed in standard code and SQL.
Source, processing, storage, governance and consumption have clear responsibilities.
Vendor services are selected for capability without hiding the architecture behind them.
Interfaces, tests and documented logic preserve a practical path to evolve the platform.
Selected work shows how I approach difficult data problems: diagnose the constraint, design the response, deal with the hard parts, and measure what changed.
A governed path from AWS-hosted MySQL through Azure Databricks and Delta Lake into ML and secure embedded reporting.
Replaced full-table reloads with change-aware ingestion and Delta Lake merge patterns so downstream marts stayed current.
Scheduled file movement between Azure Data Lake and external FTP endpoints, with Azure Key Vault handling credentials.
Customer value and lifecycle segments consolidated into a reusable analytical model.
Bookings, passengers and sales unified into a comparable reporting model.
Operational coverage, time and crew performance brought into one drillable view.
Traced negative inventory to source, reconstructed history and established a reliable baseline.
I prefer a pragmatic progression: understand the question, inspect the evidence, make the decision, then build only what the evidence justifies.
Clarify the decision, constraint and definition of success.
Inspect presence, grain, lineage, quality, frequency and feasibility.
Separate what should be fixed, engineered, sourced or deferred.
Build the reliable path, controls and analytical models.
Expose governed analytics and BI with definitions people can trust.
The hard problems are usually beneath the interface: ambiguous definitions, unsuitable grain, missing history, fragmented sources, unreliable movement or a platform that has become difficult to change.
I prefer code-first, standards-based engineering where it improves ownership, testing and portability. The aim is not to avoid vendors; it is to avoid making the vendor the owner of the reasoning.
If the organisation is unsure what its data can support, reporting is unreliable, pipelines are becoming difficult to maintain, or a platform decision needs stronger evidence, we can start with the problem.
Not ready to dive into specifics? Pick “Coffee chat” below for an informal introduction instead.
Hourly engagements from $25/hour (USD) · project work scoped and quoted separately.