Roles · Engineering
Founding Data Platform Engineer
Latin America will spend this decade deciding whether it builds frontier AI capability or rents it. The region holds the energy, the minerals, the data, and the talent of 660 million people, and almost none of the compute, the models, or the governance leverage. Futuros exists to change that.
Futuros is the evidence platform for that argument: a live, cited, trilingual observatory of 25 countries built to put verifiable numbers in front of heads of state, CELAC, the IDB, CAF, ECLAC, and the private sector. Every claim on the platform resolves to a source, and the platform is in production today.
Platform in early access for partners and funders. We will walk you through it live during the process.
The role
The founding data platform engineer owns the evidence pipeline: every number on the platform, from the moment an upstream source publishes it to the moment it renders with a citation. Ingestion, registry, provenance, freshness, and quality. The platform's credibility with governments rests on this work being right.
What you'll do
- Own ingestion. Hundreds of statistical, institutional, and news sources, each with its own format, cadence, auth quirks, and failure modes. World Bank to CEPALSTAT to a ministry's xlsx.
- Design the registry. Sources, indicators, and citations as structured, versioned data. The registry is the contract the whole platform builds against.
- Keep provenance unbroken. Every figure resolves to a source record with a working deep link. Maintain that guarantee across refreshes, merges, and re-bakes.
- Build the quality gates. Detect silent upstream failures, stale data presented as fresh, unit drift, and sign flips before they ship. A pipeline that writes "my fetch failed" as "there is nothing there" is the enemy.
- Scale the corpus. Take the source universe from hundreds to thousands without weakening any of the above.
- Serve it fast. Baked static APIs, caching layers, and an offline-capable data layer that works on a minister's phone in a dead zone.
What we're looking for
- Data platform depth. You have built and operated pipelines over messy heterogeneous sources at scale, and you treat data quality as an engineering property with tests and gates.
- Engineering fundamentals. Strong TypeScript or Python, comfortable with SQL and columnar tooling; you ship end to end.
- AI-native. You use agents and models as leverage in pipeline work: extraction, validation, triage.
- Skepticism as a habit. You check the upstream before trusting the cache, and you would rather flag a gap than fill it with a guess.
- Founding temperament. You are comfortable owning ambiguous problems with no one above you to escalate to.
Nice to have
- Working Spanish. Strongly preferred; most of our sources publish in it.
- Roots or deep working ties in Latin America.
- Familiarity with official-statistics ecosystems: national statistics offices, SDMX, ECLAC and World Bank APIs.
- Working statistics: indices, uncertainty, and why a composite can lie.
- An open-source track record.
How this works
This is a founding conversation. We are open on compensation, equity, and scope for the right fit, and we come to the table with strong institutional backing. We will be transparent about terms from the first call and expect the same directness back.
How to apply
Send a short note on your interest in the role, an example of what you've built that's aligned with the role, and why this is the dream role for you.
marcus@ladp.io