Consulting
Analytics infrastructure and reporting systems that give your team accurate, trusted data — and the tooling to answer business questions without filing a ticket to engineering.
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Response time
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Projects delivered
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Years in production
What it is
Data analytics infrastructure encompasses the data pipelines, warehouse, transformation layer, and BI tooling that allow business users to query, visualise, and monitor operational and strategic metrics from across the organisation in a consistent, governed environment.
What you get
Most organisations have more dashboards than they have people who trust them. Conflicting numbers between reports, metrics defined differently by different teams, data that is 24 hours stale by the time it reaches a dashboard, and BI tools that require SQL to answer any question not pre-built by engineering. We fix the underlying data infrastructure that causes these problems.
We build analytics stacks on the modern data stack: dbt for transformation and documented metric definitions, Snowflake or BigQuery as the analytical warehouse, and Looker, Metabase, or Power BI as the BI layer depending on your team's technical level and reporting requirements. Every metric is defined once in the semantic layer, not duplicated across dozens of individual reports.
Self-service analytics is the goal. We design data models and BI tooling so that analysts and business users can answer their own questions without an engineering ticket — while data governance controls prevent sensitive data from being inadvertently exposed and ensure metric definitions remain consistent.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Self-service analytics with role-appropriate BI tooling
Data quality monitoring with automated alerting
Multi-source data integration and historical backfill
Cohort analysis, funnel analytics, and retention reporting
Training for analyst and business user self-service
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
Built with
If your dashboards have conflicting numbers, if business users constantly question whether a report is correct, if every new dashboard requires an engineering sprint, or if your reporting runs slowly on transactional databases, the infrastructure is the problem. New dashboards built on the same foundation will have the same problems.
Through a semantic layer: a single file in version control that defines every metric — what data it comes from, how it is calculated, how it filters — and from which every downstream report is generated. When the definition of "active user" changes, it changes in one place and propagates everywhere automatically.
An initial stack with core data sources connected, transformation models built, and key dashboards live: 6–8 weeks. A comprehensive analytics platform covering multiple business units, complex data models, and full self-service: 12–16 weeks. We prioritise the highest-value reporting first and add coverage incrementally.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
