Data Engineering Lead

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Yalo

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 Data Engineering Lead to lead the team that powers the data and ML infrastructure behind these experiences.

This is a critical leadership role responsible for owning our modern data platform, evolving our architecture, and enabling the deployment of real-time AI agents that personalize, predict, and automate business outcomes at scale. If you're passionate about building resilient systems, deploying ML to production, and enabling intelligent agents through rich data semantics — this one’s for you.

Your mission?

Own and scale the infrastructure that enables autonomous, intelligent, and privacy-compliant AI agents. You will lead a team of Data Engineers and Machine Learning Engineers to ensure that data flows seamlessly, models deploy reliably, and architecture evolves strategically to support Yalo’s growth

What are the responsibilities for this role? 🧠

• Own and evolve Yalo’s data & ML platform, ensuring 99.9% uptime and clear SLAs across analytics and AI use cases.
  
  
• Define and implement a modular, real-time, and agent-ready data architecture, optimized for both humans and language models.
  
  
• Lead and mentor a hybrid team of Data Engineers and ML Engineers, driving roadmap execution and cross-functional alignment.
  
  
• Deploy machine learning and AI systems into production, with full observability, rollback strategies, and real-time feedback loops.
  
  
• Build and maintain a semantic and annotated data layer, enabling agents and humans to reason over core business concepts.
  
  
• Drive cost governance across data tools like BigQuery, DBT, Kafka, and Airflow, improving infrastructure cost efficiency by 20%+.

• Collaborate with Product, AI, and Ops teams to ensure data and model infrastructure meets business and user needs.

Job Requirements?💻

• 7+ years of experience in Data Engineering, ML Engineering, or Infrastructure roles.
  
  
• 3+ years leading technical teams (including Data Engineers or MLEs) in production environments.
  
  
• Deep experience with modern data stacks: Airflow, DBT, BigQuery, Kafka, and GCP-native tools.
  
  
• Strong understanding of streaming pipelines, orchestration, ELT, and real-time data systems.
  
  
• Experience deploying ML systems in production, including monitoring, drift detection, and A/B testing.
  
  
• Proficiency in Python, SQL, and infrastructure-as-code (Terraform or equivalent).
  
  

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 – You’re accountable from vision to uptime. No excuses.
  
  
• Clarity communicator – You explain complex systems in business terms and align stakeholders without jargon.
  
  
• Growth mindset – You learn fast, unlearn faster, and welcome feedback like oxygen.
  
  
• Proactivity – You don’t wait for roadblocks; you prevent them.
• Customer obsession – You care about your internal data customers as much as the end user.

Metrics to measure 📈

• 99.9% uptime of critical data/ML pipelines
  
  
• ≥2 ML/AI systems deployed to production with real-time feedback by Q1
  
  
• 95%+ of KPIs covered in semantic layer or data API
  
  
• ≥ 20% cost reduction across BigQuery, DBT, and streaming infra
  
  
• 100% of pipelines migrated to automated, observable frameworks
  
  
• Team health and delivery velocity KPIs (e.g., team NPS, cycle times)

• Unlimited PTO policy
• Competitive rewards on the market range
• Worklife-Personal Life Integration
• 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.