Work

Building production data products in FinTech, HealthTech and Banking.

Paid client engagements

Birdie

Lead Analytics Engineer (Contract) · Nov 2025–present · Health Tech SaaS, care sector

The problem: A customer-facing analytics product in the care sector needed its reliability brought up to the standard its role demanded. Data quality and pipeline stability weren't yet where they needed to be.
The build: Mapped the landscape and identified where the real reliability problems lay, then set the standards and principles for how the data models are built and organised, turning a tangled DAG into a clear, maintainable structure and delivering against that plan. Built new models with one of Birdie's largest partners to surface cost savings for their finance team, later generalised into the core product for every customer.
The outcome: Customer-reported data quality issues down 70% from their peak. The partner models now run as core product for all customers. Ongoing.

Senior Analytics Engineer (Contract) · Feb–Aug 2025 · Consumer FinTech, auto & unsecured lending

The problem: Several analyst teams were building in the same dbt project and hitting the problems that come with it. No shared definition of key metrics, changes in one team's models unintentionally breaking another's, and the same logic written in several places, so fixes were slow and definitions drifted whenever every copy wasn't updated in step.
The build: Modelled data marts across auto-lending and unsecured loan products on dbt and Snowflake, covering risk, pricing and operational analytics. This included a unified decision model that brought decision data from several decision engines into one place. Brought in as the first analytics engineering hire, set the AE strategy and trained the existing analyst community on dbt best practice. Added project-health monitoring across documentation, testing coverage and DAG complexity.
The outcome: A 25% cut in Snowflake credits and build time across the pipelines delivered. Answering a question like why a customer was declined, which used to mean tracing the case across three or four tables, dropped from 10 to 15 minutes to a single query. Lendable kept investing in the function, which has since grown from that first hire to 13 engineers.

Employment history

Head of Data Analytics · Mar 2022–Oct 2024 · B2B FinTech, BNPL & trade lending

The problem: The classic Series A-to-B story, a business that had outgrown its reporting. BI ran on a sprawl of independent, one-off queries, with no unified or consistent view of the business, so finance, operations and product weren't always working from the same numbers.
The build: Built the analytics function from the ground up, growing it to a team of five across analytics engineering, analysis and automation. Led an organisation-wide Looker rollout and defined the company's KPI framework across more than 40 stakeholders, giving every team a single, shared definition of the metrics that mattered. Delivered financial models for treasury forecasting, funds safeguarding and debt facility management, and built custom applications that automated operational and financial processes end to end.
The outcome: A portfolio of custom automations: a five-hour monthly job down to thirty minutes, a five-minute job run several times a day down to one. 80% of 50 Looker users logging in weekly within eight weeks. Quarterly OKRs delivered consistently across sales, risk and operations.

16 years in data, much of it in credit risk, commercial lending and trade finance. Full history on LinkedIn.