Data Engineer

• Build data pipelines: integrate diverse sources (HTML, CSV, relational databases, embeddings) using scalable ETLs.
• Modeling and optimization: design analytical models to generate KPIs and efficiently manage large data volumes.
• Enable AI: prepare datasets for ML, LLMs, and RAG systems, integrating AI for natural language queries.
• DataOps and scalability: apply CI/CD, continuous monitoring, and optimize infrastructure.
• Learn as much as you want about the world of alternative investment

• Results-oriented with strong technical ownership.
• Around 3-4 years of experience. xqbhyrx
• Relevant experience with SQL / PL-SQL
• Relevant experience with Python (pandas, Pyspark) and Apache spark
• Relevant experience with ETLs & E/R models
• Relevant experience working with Data & Development teams
• Experience with AWS services (EMR, Lambdas,…) or similar Cloud
• Fluency in Spanish and English

This gives extra points

• Basic experience with data science environments
• Basic experience with Vector database and with scraping engines
• Work with multi-country and multi-source data.
• Experience working in with financial data

Todos los posibles candidatos deben leer con atención los siguientes detalles de este trabajo antes de presentar una candidatura.

mscope is a applying to Microeconomics & Finance to help institutional investors identify highly promising SMEs and accelerate their growth. We’re an ambitious, talented & international team of + eager to build high-quality solutions with real impact on the economy & society.

Backed by strong investors & strategic partners across the fintech sector, we build a suite of using cutting-edge technology: AWS cloud-first architecture, Glue + Pyspark orchestration for Big Data, multi-agent AI systems on Amazon Bedrock, AI-accelerated development with, modern stack (Spring Boot, React-Redux).

The real challenge is execution: delivering fast without compromising quality. If you’re motivated by building - that impacts -, this is your place.

We are looking for a to lay the data foundations of a brand new product we are currently building.