Asks for

The ad says“- Experience with data governance and access-control frameworks (role-based access, data classification, audit trails).”
The ad says“- Deep, hands-on expertise with modern data stacks: Airflow, dbt, BigQuery, Kafka, and GCP-native tooling.”
The ad says“- Deep, hands-on expertise with modern data stacks: Airflow, dbt, BigQuery, Kafka, and GCP-native tooling.”
The ad says“- Experience designing and operating semantic/metrics layers (dbt Semantic Layer, LookML, Cube, or equivalent) serving multiple downstream consumers.”
The ad says“- Experience with data governance and access-control frameworks (role-based access, data classification, audit trails).”
The ad says“- Deep, hands-on expertise with modern data stacks: Airflow, dbt, BigQuery, Kafka, and GCP-native tooling.”
The ad says“- Strong understanding of streaming pipelines, orchestration, ELT, and real-time data systems.”
The ad says“- Deep, hands-on expertise with modern data stacks: Airflow, dbt, BigQuery, Kafka, and GCP-native tooling.”
The ad says“- Proficiency in Python, SQL, and infrastructure-as-code (Terraform or equivalent).”
The ad says“- Deep, hands-on expertise with modern data stacks: Airflow, dbt, BigQuery, Kafka, and GCP-native tooling.”
The ad says“- Experience designing and operating semantic/metrics layers (dbt Semantic Layer, LookML, Cube, or equivalent) serving multiple downstream consumers.”
The ad says“- Familiarity with ML systems in production well enough to architect data for them — monitoring, drift detection, A/B testing context — without owning the model deployment itself (that's a separate function).”
The ad says“- Strong understanding of streaming pipelines, orchestration, ELT, and real-time data systems.”
The ad says“- Proficiency in Python, SQL, and infrastructure-as-code (Terraform or equivalent).”
The ad says“- Experience designing and operating semantic/metrics layers (dbt Semantic Layer, LookML, Cube, or equivalent) serving multiple downstream consumers.”
The ad says“- Proficiency in Python, SQL, and infrastructure-as-code (Terraform or equivalent).”
The ad says“- Strong understanding of streaming pipelines, orchestration, ELT, and real-time data systems.”
The ad says“- Proficiency in Python, SQL, and infrastructure-as-code (Terraform or equivalent).”

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.

Job Requirements? 💻

Education: Bachelor's or Master's in Computer Science, Data Engineering, or a related field — or equivalent hands-on experience.

Experience: 8+ years in Data Engineering or Analytics Engineering, including significant time operating at a staff/principal level of technical ownership (architecture decisions, cross-team standards) rather than as a people manager.

Technical Skills:
- Deep, hands-on expertise with modern data stacks: Airflow, dbt, BigQuery, Kafka, and GCP-native tooling.
- Strong understanding of streaming pipelines, orchestration, ELT, and real-time data systems.
- Proficiency in Python, SQL, and infrastructure-as-code (Terraform or equivalent).
- Experience designing and operating semantic/metrics layers (dbt Semantic Layer, LookML, Cube, or equivalent) serving multiple downstream consumers.
- Familiarity with ML systems in production well enough to architect data for them — monitoring, drift detection, A/B testing context — without owning the model deployment itself (that's a separate function).
- Experience with data governance and access-control frameworks (role-based access, data classification, audit trails).

Problem-Solving: Comfortable owning ambiguous, cross-team architecture problems — this role exists specifically because "who owns which stage of the data pipeline" hasn't had a clear technical owner.

Soft Skills that matter to us 🫀

- Agentic mindset – You think in systems that act, not just flow. You build infrastructure to enable autonomy, not just availability.
- Ownership – Accountable from architecture to uptime, without needing direct reports to get there.
- Clarity communicator – Explains complex systems in business terms and aligns stakeholders without jargon.
- Influence without authority – Sets technical direction and raises the bar for teams you don't manage, through credibility and documentation.
- Proactivity – Doesn't wait for roadblocks; prevents them.
- Customer obsession – Cares about internal data customers (CS, Product, leadership) as much as the end user.

Metrics to measure 📈

- 99.9% uptime of critical data pipelines.
- 95%+ of core KPIs covered by the semantic layer or a governed data API (vs. raw/ungoverned sources).
- ≥20% cost reduction across BigQuery, dbt, and streaming infrastructure.
- 100% of critical pipelines migrated to automated, observable frameworks.
- Time-to-build for new dashboard/agent data needs, trending down as the semantic layer matures.

What do we offer? 🥰

- Unlimited PTO policy
- Competitive rewards on the market range
- Remote/Hybrid working available (Córdoba preferred; Guatemala City, São Paulo, Mexico City also open)
- Flexible time (driven by results)
- Start-up environment
- International teamwork
- You and nothing else limit your career here

We care,
We keep it simple,
We make it happen,
We strive for excellence.

At Yalo, we are dedicated to creating a workplace that embodies our core values: caring, initiative, excellence, and simplicity. We believe in the power of diversity and inclusivity, where everyone's unique perspectives, experiences, and talents contribute to our collective success. As we embrace and respect our differences, we strive to create something extraordinary for the benefit of all.
We are proud to be an Equal Opportunity Employer, providing equal opportunities to individuals regardless of race, color, religion, national or ethnic origin, gender, sexual orientation, gender identity or expression, age, disability, protected veteran status, or any other legally protected characteristic. Our commitment to fairness and equality is a fundamental pillar of our company.

At Yalo, we uphold a culture of excellence. We constantly challenge ourselves to go above and beyond, delivering remarkable results and driving innovation. We encourage each team member to take initiative and make things happen, empowering them to bring their best ideas forward and contribute to our shared goals.