Data Platforms & Engineering.
The data layer underneath everything else: ingestion, enrichment, governance, and the infrastructure that analytics and AI stand on.
Every intelligent system is a data system first.
AI strategies fail quietly in the data layer. Models are only as good as the pipelines behind them, and dashboards are only as honest as the governance underneath. Institutions that skip the data work end up automating their own blind spots.
Disnesta designs and builds the data foundation as an engineered system: pipelines that are observable, enrichment that is documented, and governance that is enforced in the architecture rather than in a policy PDF.
The firm runs its own data platforms behind its published indices and reports — the same standards we hold in client work.
Four areas of work.
Data platform design
Platform architecture across ingestion, storage, transformation, and serving — sized to the institution, not to a vendor reference diagram.
Pipelines & integration
Reliable ingestion and integration across internal systems and external sources, with data quality treated as an engineering requirement.
Data governance
Lineage, access, retention, and quality controls that satisfy boards and regulators — built into the platform.
Analytics & reporting
The reporting and analytics layer that turns the platform into decisions, from dashboards to published reports.
Ways to work with the firm.
Data estate review
A systems reading of the current data estate: what is trustworthy, what is fragile, and what the priorities are.
Platform delivery
Design and build of the platform or pipeline, to production standards, documented for your team.
Operate & transfer
Operational instrumentation and handover so the platform runs without us.