Work we're proud of.

Untangling analytics for a 100+ person SaaS company

They'd inherited what most teams inherit — duplicated definitions, brittle pipelines, a backlog growing faster than the team. We didn't pitch a moonshot. We helped them rebuild the architecture so the numbers were consistent and the work was shippable. Boring outcomes, but the right ones.

The client is a fast-growing HR SaaS company headquartered in Europe. By the time they engaged us, their analytics function had been running for several years and had accumulated the typical signs of organic growth: the same business entity defined inconsistently across multiple tables, fragile pipelines that broke under reasonable load, and a single point of knowledge concentrated in two long-tenured engineers. New report requests took up to a month to deliver.

We embedded two senior engineers with their team for six months. The first six weeks were spent on discovery — mapping the existing architecture, cataloguing data definitions across the business, and identifying the highest-leverage points for intervention. We then proposed a three-phase remediation plan, prioritised by business impact rather than technical interest.

• Established a single source of truth for each core business entity, with documented ownership
• Replaced duplicated transformation logic with a shared semantic layer in dbt
• Refactored the most fragile pipelines, prioritising those that fed business-critical reporting
• Introduced lineage tracking and data quality monitoring with clear ownership for each domain
• Worked alongside the existing team throughout, with paired review on every meaningful change

• Consistent definitions across reporting, finance, and product analytics
• Time-to-deliver for new analytical requests reduced substantially
• The internal team able to onboard new analysts without months of context transfer
• A documented architecture the team could continue to extend after our departure

A new data platform for a fast-growing analytics startup

Their data volumes had quietly outgrown a platform that was never meant to carry them. We built a new one from scratch, designed around how the business actually runs — and handed it over so their team could run with it themselves.

The client is a US-based real estate analytics company that had grown rapidly in customer base and the volume of property data they processed. Their original setup — a small set of scripts feeding a single database — had served them well in the first two years but was now the limiting factor on both reliability and product velocity. Adding a new data source took weeks. Recovery from a failed run was manual.

We delivered a complete data platform across five months, designed end-to-end around their specific business model. The architecture was deliberately conservative: well-understood components, conventional patterns, generous documentation. We wanted the team to be able to operate, extend, and reason about the system without us.

• Two-week discovery to model the business domain and identify the analytical questions that actually mattered
• Architecture design phase with explicit trade-off documentation, reviewed with their leadership
• Phased build — ingestion, transformation, modelling, observability — with each phase shippable in isolation
• Final phase dedicated entirely to handover, documentation, and pairing with their internal hires

• Cloud-native warehouse with separated ingestion and transformation layers
• Idempotent pipelines with automated recovery, designed to absorb upstream variability
• Clear separation between raw, modelled, and consumption layers — each with explicit contracts
• End-to-end lineage and quality monitoring, with alerts routed to the team responsible for each domain
• Cost dashboards from day one, so the team could see and reason about cloud spend

• Platform delivered on schedule and handed over to the internal team
• New data sources now onboardable in days rather than weeks
• Recovery from upstream failures automated and observable
• Internal team able to extend the platform independently from month one of post-handover