Data Engineer LATAM

About MDOTM

MDOTM is a global leading provider of AI-driven investment solutions, growing across our offices in London, Milan, and New York. We recently closed a$27M growth equity round, led by Expedition Growth Capital, to support our continued international expansion. Our proprietary platform, Sphere, currently supports over $100B in assets under management for institutional clients including Morgan Stanley, Amundi, and Zurich Bank.

Role Overview

We are looking for a Data Engineer to play a key role in shaping and scaling our platform as the organization grows in complexity and data maturity.

In this role, you will be responsible for designing and evolving the core data infrastructure that powers analytics, machine learning, and critical business decisions. You will take ownership of how data flows across the company, from ingestion to consumption, ensuring it is reliable, well-modeled, and accessible to a wide range of stakeholders.

Key Responsibilities

• Design, build, and maintain production-grade data pipelines and data-intensive systems
• Configure, schedule, and monitor data pipeline execution to ensure reliability, maintainability, and timely delivery across all data processes
• Deploy and manage data infrastructure on AWS or on-premises, ensuring scalability, security, and cost-efficiency
• Monitor and optimize relational and NoSQL database performance (e.g., MySQL, PostgreSQL, MongoDB) to ensure efficient querying, indexing, and data access at scale
• Ensure reliable ingestion, transformation, and availability of large-scale and time-series financial data, considering financial-specific data quality characteristics.
• Implement and maintain data quality, validation, and monitoring mechanisms across data workflows
• Define and evolve data models and storage structures across relational and NoSQL systems
• Collaborate with Data Scientists and Analysts to ensure accurate and efficient data access for analytics and modeling use cases
• Optimize data processing workflows for performance, reliability, and operational stability
• Troubleshoot complex data issues and drive root cause analysis.
• Contribute to technical decision-making and help define the roadmap for data infrastructure.

Requirements

• Degree in Computer Science, Engineering, or a related field.
• Strong programming skills in Java or Python (or other JVM-based languages)
• Strong understanding of data modeling, SQL, and NoSQL databases
• Strong focus on data quality, validation, and monitoring practices
• Experience working with large-scale and time-series datasets in financial contexts, with an understanding of their structure and common data quality challenges
• Experience building and maintaining production data pipelines or data-intensive systems, with focus on reliability and performance
• Ability to troubleshoot and optimize production data systems
• Experience with workflow orchestration and data pipeline scheduling tools
• Solid understanding of financial data structures and concepts, with the ability to model and validate financial data accurately
• Strong problem-solving skills and attention to detail.
• Professional proficiency in English.

Bonus Points

• Experience with monitoring and visualization tools (e.g., dashboards)
• Exposure to machine learning workflows and related data requirements

Why Join Us?

• Work at the leading edge of technology, leveraging our decade of experience in proprietary AI to build the next generation of industry-defining tools.
• Competitive salary & trulyflexible work environment.
• Benefit from an unlimited learning and development budget to stay at the bleeding edge of AI research, alongside a fast-track path into technical leadership or principal research roles.
• Collaborate daily with anultra-international team(18+ nationalities) spread across our offices in Milan, London and New York.
• Annualcompany retreatat a stunning location.
• Fast-trackcareer progression, with opportunities to grow into leadership roles.

Originally posted on Himalayas