Selected Work
Products that moved the needle.
I build and scale the supply-side infrastructure that powers marketplace revenue and buyer experience. The data pipelines, APIs, and listing systems I own feed directly into what buyers search, discover, and act on. Here are four flagship initiatives.
Case Study 01
Re-Architecting a Partner API Ecosystem Serving 2,000+ Sellers
Transforming a fragile integration layer into a scalable partner platform that drives seller adoption and powers buyer-facing data quality across the marketplace.
Reduced manual entry time
Increased API adoption
Sellers enabled
Higher structured data completeness
New buyer search filters enabled
Faster cross-functional decisions
The Challenge
Our marketplace's dealer ecosystem depended on fragmented legacy integrations. The API layer was mission-critical, powering thousands of listings and feeding the structured data that buyer-facing systems (search, recommendations, listing pages) depend on.
The system was operationally brittle, difficult to scale, high-friction for onboarding, and generating integration-related support load. This wasn't a feature gap, it was a platform constraint limiting both seller efficiency and the data quality buyers depend on.
What I Did
Designed the Next-Gen API Platform
Led a full re-platforming of partner-facing APIs, shifting from manual entry dependency to structured data-driven ingestion for 2,000+ professional sellers.
Owned Reliability as a Product Metric
Treated uptime, data consistency, and integration tickets as first-class product KPIs. Implemented observability and adoption tracking across endpoints.
Orchestrated Enterprise Migration
Aligned engineering, product support, sales, and key accounts to reduce rollout risk, managing system transition without disrupting marketplace liquidity or the buyer-facing data quality downstream teams depend on.
Impact: Converted a fragile integration layer into a scalable partner platform. Structured data completeness improved by 20%, enabling 10+ new buyer search filters with over 80% data coverage across the marketplace.
Case Study 02
Scaling the Listing Platform to Increase Liquidity and Dealer Adoption
Increasing inventory velocity, improving seller efficiency, and strengthening marketplace liquidity through automation and experimentation.
Faster time-to-market
Dealer adoption increase
Listing completion rate
Reduced seller drop-offs
VIN lookup adoption
Fewer manual data errors
The Challenge
Dealer workflows were slowing inventory velocity. Listing creation was too manual, too slow, and too inconsistent. In a marketplace, speed equals liquidity, and liquidity equals revenue.
Time-to-market for inventory was slow, sellers experienced friction during listing completion, structured data quality was inconsistent, and experimentation maturity was low across the platform.
What I Did
Automated Core Listing Workflows
Scaled structured listing infrastructure, cutting average listing creation time from 15 minutes to 6.5 minutes and improving ingestion quality.
Built an Experimentation Engine
Designed a structured A/B testing framework embedded directly into listing workflows. Moved the organisation from feature releases to measurable iteration.
Strengthened Vehicle Data Services
Optimised VIN identification and vehicle data services used by external partners, increasing structured data adoption and reducing manual errors.
Impact: Increased inventory velocity and seller efficiency. Higher listing completeness reduced listing page bounce rates by 34% and improved search relevance for buyers across the marketplace.
Case Study 03
Launching AI-Powered Listing Descriptions in a Marketplace Core Flow
Building a generative AI product from scratch inside the marketplace listing flow, with no prior AI infrastructure, no validated demand, and no internal playbook.
Increased listing engagement
Higher lead conversions
Reduced seller content burden
The Challenge
Dealer-generated listing descriptions were inconsistent, low quality, and performance-sensitive. There was no AI infrastructure in place: no generative workflows, no clear adoption path, and no experimentation baseline.
This was not an optimisation project. It was a zero-to-one product with no validated user demand, no AI content governance model, no clarity on whether dealers would trust automation, and risk of harming engagement if quality dropped.
What I Did
Framed a Clear Business Hypothesis
Defined the product bet upfront: if we automate structured listing descriptions aligned with vehicle attributes, we should increase engagement and lead conversion while reducing seller effort. Clear hypothesis, clear measurable impact.
Built the Experimentation Layer First
Before scaling AI output, designed success metrics, defined quality guardrails, structured A/B validation, and ensured reversibility. Shipped controlled experiments before full rollout.
Embedded Into Workflow With Minimal Friction
Integrated AI suggestions directly into listing creation rather than forcing review-heavy editing flows. Treated adoption as a product design problem, not a technology problem.
Impact: Proved that improving seller-side content quality directly lifts buyer engagement and lead conversion. Built hypothesis-first and embedded into core workflows, this became a lever for both sides of the marketplace.
Case Study 04
Building Mission-Critical HR Data Infrastructure for a Global Enterprise
Designing enterprise-grade API infrastructure that became the data backbone for payroll, workforce operations, compliance, and cross-department reporting.
Process efficiency improvement
Business units on one platform
Compliance incidents post-launch
Faster downstream development
Faster release cycles
Fewer post-launch issues
The Challenge
A global pharmaceutical enterprise relied on fragmented, legacy HR systems that couldn't support modern workforce operations. Payroll, compliance reporting, and cross-department data access were bottlenecked by inconsistent data flows and manual processes.
The organisation needed a unified data backbone: a set of core HR APIs and automated data pipelines that enable real-time access, reduce compliance risk, and unlock downstream innovation across the enterprise.
What I Did
Built Core HR APIs & Data Pipelines
Designed and delivered a suite of enterprise-grade APIs that standardised how HR data was accessed across payroll, workforce planning, and compliance, replacing fragmented manual processes with reliable, governed data services.
Automated Data Access & Governance
Implemented automated data provisioning and governance controls, reducing manual data requests and ensuring consistent, auditable access across business functions in a regulated environment.
Defined the Product Roadmap Across Competing Workstreams
Owned prioritisation and trade-off decisions across a CHF 2.5M investment spanning multiple workstreams, balancing immediate operational needs against long-term platform scalability in a heavily regulated environment.
Impact: Transformed fragmented HR systems into a unified data platform that powers payroll, compliance, and workforce operations across the enterprise.
Interested in what I could do for your team?
I'm always open to discussing product strategy, platform challenges, or new opportunities.