Software Engineer | Hedge Fund | Market Data

Mondrian Alpha; Greater London; Sur place; GBP 100 000 - 180 000; Plein temps

Résumé du poste

Mondrian Alpha is seeking an experienced Software Engineer to own the data platform as it scales. You will work directly with the CTO and a small team to shape the data layer, moving across the stack as needed.

This role emphasizes ownership of data infrastructure over narrow specialization and offers relocation support as the firm grows. You will engage with traders and researchers, design storage architectures, ensure data quality, and build reliable data pipelines.

Qualifications

• Demonstrated ownership of infrastructure systems and data platforms.
• Ability to discuss production data systems, SQL, data modelling, ETL, and reliability at a technical level.
• Software engineering discipline with focus on design, testing, and maintainability.
• Comfort operating across multiple technology areas beyond narrow specialization.

Responsabilités

• Own data ingestion, ETL pipelines, and vendor dataset management.
• Design and evolve storage architecture, schemas, and metadata management.
• Establish data quality frameworks and monitoring/observability systems.
• Ensure reliability and performance of critical data flows.
• Make data accessible to traders, researchers, and other systems via APIs/notebooks.

Connaissances

Data infrastructure ownership

Cross-functional collaboration

Production data systems

Observability & monitoring

Outils

SQL

ETL pipelines

APIs

Notebooks (Jupyter)

Data modelling

Description du poste

Our client, a well-funded, early-stage systematic investment firm is seeking an experienced Software Engineer to own its data platform as it scales. Supported by only a small team, the CTO has built the platform alone and needs someone to take on more ownership of the data as the firm enters its next growth phase.

This role is not for a pure data engineer. The firm is seeking an excellent software engineer who can own data infrastructure; someone comfortable moving across multiple areas of the technology stack when required.

Scale and impact: You're not one of 200 engineers in a data team. You're joining a small firm where your architectural decisions shape the platform. You'll see how your work directly affects the firm's ability to trade and make investment decisions.

Ownership: You own the problem, not a ticket. The CTO is there to collaborate and mentor, but you're taking increasing responsibility for the data layer as it evolves.

Breadth: The firm deliberately doesn't want narrow specialists. If a critical problem emerges in core systems, research tooling, or trading infrastructure, you'd get involved. This is a feature, not a bug.

Proximity to business: Unlike large institutions, you'll work directly with traders and researchers. You'll understand why data matters to them, and they'll understand what you're building.

Location: The firm is based in Monaco, with comprehensive relocation support and visa assistance provided. Existing staff members also live in Italy, or France.

Key Responsibilities

• Take ownership of data ingestion, ETL pipelines, and vendor dataset management.
• Design and evolve storage architecture, schemas, and metadata management.
• Establish data quality frameworks and monitoring/observability systems.
• Ensure reliability and performance of critical data flows.
• Make data accessible to research teams, traders, and other systems (APIs, notebooks, self-service tooling).
• Get involved in adjacent engineering problems- core systems, APIs, research infrastructure- as priorities shift.
• Partner directly with the CTO and investment team to understand business needs.

Ideal Background

Experience: 5+ years in relevant roles.

Ideal candidates typically come from:

• Hedge funds or systematic/quantitative trading environments.
• Prop trading or high-frequency trading operations.
• Investment banking (front-office technology).
• Market data vendors or sophisticated financial data platforms.

What matters more than background:

• Demonstrated ownership of infrastructure systems (not just contribution to larger projects).
• Comfort discussing production data systems, SQL, data modelling, ETL, reliability at a technical level.
• Software engineering discipline – proper design, testing, maintainability.
• Willingness to operate across multiple technology areas, not just a narrow speciality.
• Genuine interest in being close to the business and understanding how technology drives investment outcomes.

Desirable but not required:

• Jupyter and research-facing data tooling.