000

snyamson

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.

Solomon Nyamson

What I do

End-to-end data work, from the field form to the figure a donor signs off on.

  • Monitoring & evaluation

    Logframes and indicator libraries turned into tested data models, so every number traces back to a definition.

  • Humanitarian data

    Needs assessments and beneficiary registries cleaned, de-duplicated and geocoded, ready for cluster reporting.

  • ESG & impact reporting

    GRI and IFRS S1/S2 disclosures modelled as auditable pipelines, with lineage from raw reading to published figure.

  • Analytics engineering

    Warehouses, dbt models and tested transformations your team can query without waiting on me first.

Services

Take one piece, or the whole chain from field form to signed-off figure.

  • Analytics engineering

    dbt models, tested transformations and a warehouse your team can query without asking me first.

  • M&E framework design

    Indicator definitions, disaggregation rules and reporting calendars agreed before a single form is built.

  • Survey & data collection

    ODK, KoBo and CommCare instruments with validation, offline sync and clean export straight into the pipeline.

  • Dashboards & BI

    Power BI and Superset reporting built around the decision, not around whatever the source system happened to store.

  • ESG reporting

    Emissions, workforce and supply chain metrics assembled to a disclosure standard, with evidence attached.

  • Data quality & governance

    Automated tests, anomaly checks and documented ownership so bad data is caught long before a donor sees it.

My process

A clear roadmap to the answer.

  1. Discover

    Understanding the programme, the decisions, and the data you already hold.

  2. Define

    Indicator definitions, disaggregation and reporting calendar agreed in writing.

  3. Model

    Sources mapped into a tested warehouse layer with documented lineage.

  4. Build

    Pipelines, quality tests and dashboards built around the decision, not the source system.

  5. Handover

    Documentation, ownership and training so the reporting outlives the engagement.

Contact

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