Built ETL pipelines, flat table architecture, and internal AI tools that made teams self-sufficient
The Problem
As Intellect grew, the analytics workflow was entirely manual and bottlenecked on the data team. Monthly provider payouts for 500+ providers were calculated manually by finance. Event data was scattered across multiple systems with no standardised taxonomy.
Product and growth teams filed tickets for every data request, creating a queue that slowed decision-making. There was no self-serve capability.
500+ provider payouts calculated manually by finance each month
Event data scattered across systems with no standardised taxonomy
Every data request required a ticket, creating long queues
Zero self-serve capability across the organisation
The Approach
Rather than just building dashboards, I focused on building the infrastructure layer that would make the entire organisation data-self-sufficient — pipelines, standardised data models, and tools that non-technical teams could use independently.
Give teams the tools and data to answer their own questions
What I Built
Automated monthly payouts for 500+ providers, integrating base pay, bonus logic, and utilisation tracking. Replaced a fully manual finance workflow that took days each month.
Designed a standardised event taxonomy and flat table structure across the platform. Every product event, session event, and operational metric flowed into clean, queryable tables.
Built an internal tool for the Revenue and Partnerships team that takes deal inputs and outputs client pricing using embedded business logic. Replaced a complex manual spreadsheet process.
Built a batch simulator, interactive CLI, and sensitivity analyser so product and growth teams could run scenario testing on the recommendation algorithm independently without filing a data request.
The Result
Analysis overhead cut — teams self-served instead of waiting for data team
Monthly payout process went from days of manual work to automated pipeline
Standardised event taxonomies enabled consistent metrics across all teams
Product and growth teams ran 3x more experiments with self-serve tools
Business Impact
Automated provider payout calculations eliminated a recurring bottleneck for the entire finance team.
Teams no longer waited for data requests. Self-serve tools put the answers directly in the hands of decision-makers.
Standardised taxonomy meant every team was working from the same source of truth. No more conflicting numbers.
The flat table architecture, pipelines, and tooling became the layer everything else was built on.
Looking for a data person who can go from SQL to boardroom? I'd love to chat.