Our client, a well-funded, early-stage systematic investment firm, is seeking an experienced Software Engineer to own its data platform as it scales. The CTO has built the platform largely alone, and the firm now needs someone to take end-to-end ownership of the data layer: ingestion, ETL pipelines, vendor dataset management, storage design and data quality across market data, reference data and historical time series.

This is a software engineering role with a strong data focus, not a pure data engineering position. You’ll bring production-grade Python, expert SQL (data modelling, query optimisation, indexing), and experience with both relational and non-relational storage. Orchestration, monitoring, CI/CD and cloud experience (Azure preferred, AWS or GCP relevant) will help you make the platform more reliable and observable. You’ll also be comfortable moving across the stack when a critical problem emerges in core systems, APIs or research tooling.

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, and you’ll see directly how your work 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’ll take 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.

Key Responsibilities

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

Ideal Background

Experience: 3–5 years in relevant data-focused engineering roles.

Ideal candidates typically come from:

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

What matters more than background:

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

Desirable but not required:

1. Experience with orchestration tools (Airflow, Dagster, Prefect) or streaming platforms (Kafka, Azure Event Hubs).
2. C# / .NET experience.
3. Redis, Cassandra or distributed database knowledge.
4. Container orchestration (Docker, Kubernetes).
5. Jupyter and research-facing data tooling.
6. Experience with financial market data, particularly fixed income and derivatives.

If working on critical data infrastructure in an early-stage company, with direct CTO and investment team exposure, interests you, either apply directly or send your CV to .