
Business intelligence platform
Enterprise data platform • Analytics engineering

000
snyamson
+72
Indicators modelled
+15
Programmes supported
It's Solomon — an analytics engineer & sustainability expert
I turn messy programme data into models, indicators and dashboards that people can act on. Monitoring and evaluation, humanitarian response analysis, and ESG reporting — built to scale.

End-to-end data work, from the field form to the figure a donor signs off on.
Logframes and indicator libraries turned into tested data models, so every number traces back to a definition.
Needs assessments and beneficiary registries cleaned, de-duplicated and geocoded, ready for cluster reporting.
GRI and IFRS S1/S2 disclosures modelled as auditable pipelines, with lineage from raw reading to published figure.
Warehouses, dbt models and tested transformations your team can query without waiting on me first.
Take one piece, or the whole chain from field form to signed-off figure.
dbt models, tested transformations and a warehouse your team can query without asking me first.
Indicator definitions, disaggregation rules and reporting calendars agreed before a single form is built.
ODK, KoBo and CommCare instruments with validation, offline sync and clean export straight into the pipeline.
Power BI and Superset reporting built around the decision, not around whatever the source system happened to store.
Emissions, workforce and supply chain metrics assembled to a disclosure standard, with evidence attached.
Automated tests, anomaly checks and documented ownership so bad data is caught long before a donor sees it.
A clear roadmap to the answer.
Understanding the programme, the decisions, and the data you already hold.
Indicator definitions, disaggregation and reporting calendar agreed in writing.
Sources mapped into a tested warehouse layer with documented lineage.
Pipelines, quality tests and dashboards built around the decision, not the source system.
Documentation, ownership and training so the reporting outlives the engagement.
Have a dataset that needs a straight answer, or a reporting cycle that keeps slipping? Tell me what decision the data has to support and I'll tell you what it takes to get there.
Available for consulting and fractional data leadership