I'm a senior data analyst with 8+ years turning large, messy datasets into decisions across banking, payments and platform businesses. I work primarily in SQL across BigQuery and other warehouses - writing complex querie…
I'm a senior data analyst with 8+ years turning large, messy datasets into decisions across banking, payments and platform businesses. I work primarily in SQL across BigQuery and other warehouses - writing complex queries and window functions to extract, clean and reshape data, then building the dashboards, MI and reporting that leadership acts on. I've owned analysis end-to-end, from scoping the question through to the recommendation and stakeholder sign-off; I define the metrics teams report against, run demand-forecasting and segmentation work, and mentor junior analysts. Most of my experience is in fraud and risk, but the craft is general analytics: querying, reporting, BI and turning numbers into clear decisions.
<p>Own analysis end-to-end: scoping the question, extracting and transforming data in BigQuery, baselining metrics, segmenting cohorts and quantifying impact to drive decisions and stakeholder sign-off.<br>
Built performance MI in BigQuery, surfaced into Google Sheets via Data Connections, with agent-performance reporting, demand analysis and demand-forecasting / seasonality models currently in development alongside executive and operations dashboards.<br>
Delivered a data-driven analysis on account-labelling data that quantified preventable volume, operational effort saved and cost reduction, adopted into the onboarding process.<br>
Led an end-to-end analytical review across a 13-month dataset that proved a production decision rule added no incremental value over existing logic, securing sign-off from five senior stakeholders to retire it.<br>
Own and chair a monthly business review, building the deck and presenting performance, trend and demand insight to ~35 stakeholders across Strategy, Product, Analytics and Operations.<br>
Rebuilt the prioritisation logic and defined the standard performance metrics now used across the team.<br>
Optimised BigQuery query design to cut compute cost on high-volume tables, improving the cost-efficiency of the analytics pipeline.<br>
Mentor junior data and insights analysts, peer-reviewing SQL and analytical logic.<br>
Use ChatGPT, Claude and Gemini as analytical assistants to speed up SQL development, documentation and analysis.</p>
<p>Produced insight and reporting across multiple portfolios using SQL and SAS, quantifying trends and surfacing emerging patterns for senior stakeholders.<br>
Automated daily, weekly and monthly reporting through SAS Managed Services, cutting manual effort and improving delivery consistency.<br>
Owned portfolio Tableau dashboards, translating business questions into delivered analysis.<br>
Partnered across analytics, portfolio and compliance teams to scope and deliver complex bespoke data requests.</p>
<p>Analysed large transactional datasets in PostgreSQL, profiling customers and transactions to surface behavioural clusters and patterns.<br>
Designed and tested decision rules, validating performance with fire-rate and error analysis and reporting outcomes back to clients.<br>
Worked with an ML-assisted analytics platform (Fraud.net) to flag bespoke and custom patterns.</p>
<p>Sole analyst - consolidated data across several systems to surface actionable insight and escalate findings.<br>
Migrated management information out of Excel and into Tableau, building a self-serve reporting tool for senior leadership.</p>
<p>Analysed data across 12 international markets (including the UK, France, Italy, Spain, Belgium, UAE and Australia) using Snowflake and Looker, surfacing trends and patterns for country General Managers.<br>
Analysed customer, restaurant and voucher datasets, working with Data Science, Engineering, Product and Customer Care to balance commercial outcomes with risk.<br>
Project-managed two analytics-led initiatives that delivered roughly late-five-figures in combined savings.<br>
Configured decisioning rules in the Ravelin platform.</p>
<p>Analysed booking and payment data to identify anomalies and validate customer identity signals.<br>
Built targeted decision rules (by country, value and route) to isolate high-risk activity.</p>
<p>Profiled accounts using merchant, transaction and location data to identify patterns and points of compromise.<br>
Investigated data anomalies to improve protection of genuine customers; used CIFAS for data sharing.</p>
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