Data Engineer

Sr. Staff Data Engineer (L7)

Yalo

Preferably candidates based in Córdoba, Argentina. Also open to Guatemala City, São Paulo, and Mexico City. This is a Hybrid role.

Hi! We're Yalo! We're on a mission to revolutionize how businesses sell in an omnichannel way with our intelligent sales platform and intelligent agents powered by cutting-edge AI. Imagine a world where businesses seamlessly connect with their customers across every channel—offering personalized experiences, anticipating needs, and delivering what they want with ease. That's the reality we're building at Yalo.

Born in Latin America and driven by its spirit of innovation, we're transforming sales for businesses around the globe. From empowering businesses in emerging markets to helping enterprises scale intelligently, we're redefining how companies engage with their customers and drive growth.

At Yalo, we believe the future of sales is personalized, omnichannel, intelligent, and conversational. Join us as we empower businesses to build stronger relationships and achieve remarkable results worldwide!

Job Summary 🧾

We're looking for a Sr. Staff Data Engineer to own the data platform that powers Yalo's analytics, dashboards, and AI agents. This is a senior technical ownership role — not a management track. You'll define the architecture, set the standards, and be the deepest technical authority on how data moves from raw platform events to governed, trusted, agent-ready information. If you think in systems that need to be reliable at 99.9% uptime, care as much about a clean semantic layer as a fast pipeline, and want your technical judgment — not your headcount — to be your leverage, this one's for you.

Your mission?

Own and evolve the infrastructure that turns Yalo's raw platform events into the trusted data foundation for dashboards, business decisions, and AI agents like IRIS. You'll work daily with Engineering (event generation), Business Analytics (dashboard consumers), and the ML/AI team (agent consumption) — without owning any of those teams — to keep the whole pipeline reliable, well-governed, and fast to build on.

What are the responsibilities for this role? 🧠

- Own and evolve Yalo's data platform, ensuring 99.9% uptime and clear SLAs across analytics and AI use cases.
- Define a modular, real-time, agent-ready data architecture — modeled for both human dashboard consumers and LLM-based agents like IRIS.
- Own the semantic layer — the single source of truth that keeps metrics consistent across Looker, Power BI, and IRIS. Prevent the dashboard sprawl and definition drift that comes from ungoverned, ad hoc modeling. - Build and maintain a semantic and annotated data layer that lets both agents and humans reason over Yalo's core business concepts, not just query raw tables. - Own data governance — the tiered, role-based access framework (CSM/PM/Ops/Power User) that routes all access through governed data. - Define stage ownership and interfaces — the explicit boundary between Data Engineering (ingestion/modeling) and Business Analytics (dashboard build), so both teams can move independently without stepping on each other's data. - Drive cost governance across BigQuery, dbt, Kafka, and Airflow — targeting 20%+ infrastructure cost efficiency improvement, continuing the team's track record (a prior tooling consolidation already cut one cost line by 95%). - Lead technical incident response when pipelines break or produce incorrect data, especially where client-facing dashboards or agent outputs are affected. - Set technical standards and mentor engineers across teams who touch the data layer — through code review, documentation, and architecture decisions, not people-management authority. - Partner cross-functionally with Engineering, the incoming Analytics PM (stakeholder synthesis), Business Analytics, and the ML/AI team to keep the full pipeline — events → ingest/model → dashboards/agents — running end to end.