Chief Data Architect

Há 3 dias

Mato Grosso, Brasil beBeeDataLeader Tempo inteiro R$900.000 - R$1.200.000
Job Overview

We're driving the development of AI-powered autonomous investment agents that deliver institutional-grade portfolio management to a broader audience.

Our platform combines cutting-edge quant models, real-time data processing, smart contract vaults, and conversational LLMs into a scalable architecture capable of running 100k+ agents in parallel.

As we rapidly move from closed Beta to global rollout, we need a technical leader who can own backend and data infra at scale.

The Role

We're seeking a Principal Engineer with deep expertise in data and backend systems to architect, optimize, and scale the core infrastructure.

You'll be hands-on designing resilient data pipelines, ensuring our quant models and LLMs run seamlessly in production, and building the backend services that power autonomous investment agents.

You'll also be a multiplier — raising engineering standards, mentoring the team, and making foundational decisions that shape the product.

Responsibilities
  • Own design and implementation of real-time data pipelines with low-latency and fault tolerance.
  • Architect backend services powering agent orchestration, portfolio optimization, and execution.
  • Optimize performance and cost efficiency across distributed systems running at scale.
  • Work closely with quant researchers and AI / LLM engineers to deploy trading algos and integrate them into the platform.
  • Collaborate with smart contract engineers to ensure vault and execution layers are tightly integrated with backend systems.
  • Define best practices in CI / CD, observability, and system reliability.
  • Mentor engineers and set the technical direction for backend and data systems.
Requirements
  • 7+ years of experience in backend and data engineering.
  • Strong experience in distributed data processing and real-time streaming systems.
  • Proven track record of building and scaling microservice architectures.
  • Deep understanding of database systems and data modeling for high-throughput environments.
  • Experience deploying and monitoring ML / quant models in production.
  • Comfort working in high-ambiguity startup environments and making architectural bets.
  • Bonus: experience with blockchain integrations, financial data systems, or LLM-driven products.

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