Job Description

The Firm

Capula Investment Management is a leading global quantitative hedge fund managing over $30 billion in assets. We are headquartered in London and have offices in New York, Singapore, Hong Kong, Tokyo, Geneva and Abu Dhabi. We manage absolute return, enhanced fixed income, macro and alpha strategies for a diversified group of investors worldwide. Capula invests in a broad universe of asset classes, including fixed income, equities, currencies and commodities, as well as derivatives related to these asset classes.

The Role

We are looking for a quantitative developer to build and operate data platforms and risk analytics systems to manage P&L, VaR, scenarios, and risk exposures across our portfolios. You’ll work closely with quantitative researchers, traders and risk managers to deliver reliable BAU services in Python while designing next‑generation, low‑latency components in C#. You will contribute to both evolutionary improvements and greenfield builds that power reporting and analysis through Python, Excel, and web-based tools.

What you’ll do

• Own BAU production services in Python running in Docker: monitor, troubleshoot, and continuously improve reliability, test coverage, and deployment pipelines.
• Manage and optimize data at scale: ingest, store, and serve large financial data sets in SQL, DuckDB/Parquet, and in‑memory databases; design schemas, partitioning, and indexing for multi‑dimensional access e.g., by instrument, portfolio, scenario and time‑series.
• Design a low‑latency alternative path in C#: build ingestion and access layers optimized for speed (efficient serialization, columnar/contiguous memory access, in‑memory stores/caches, async I/O), and integrate them with existing risk/pricing workflows.
• Propose cutting‑edge solutions: evaluate modern columnar technologies, in‑memory query engines, and vectorized execution; prototype approaches for sub‑second analytics on high‑volume tick and end‑of‑day data.
• Deliver greenfield projects end‑to‑end—from design and build to production rollout—while maintaining critical risk systems used daily by the business.
• Deploy and operate on Linux in AWS (EKS, ECS, EC2, S3) with CI/CD and Infrastructure‑as‑Code

Duties & Responsibilities

• Development & maintenance of risk systems and data services used for P&L, VaR, scenarios, and exposure reporting.
• Performance engineering: query tuning, profiling (CPU/IO), memory‑aware data layouts, in‑memory caching strategies, and fast analytics.
• Collaboration with quants, risk, and trading; clear communication of trade‑offs and design decisions.

Requirements

• 3-7 years in quantitative development, systems or platform engineering roles.
• Strong Python (production code, packaging, testing).
• Hands‑on with SQL (schema design, query optimization) and DuckDB/Parquet for large analytical datasets.
• C# skills building low‑latency data services (async I/O, efficient collections/serialization, profiling).
• Time‑series and multi‑dimensional data modeling and access patterns.
• Experience with in‑memory databases/caches for ultra‑low‑latency analytics.
• Production experience on Linux and AWS (operability, monitoring, CI/CD).
• Full software lifecycle ownership; excellent analytical/problem‑solving and communication skills.

Benefits

Capula is dedicated to helping all employees flourish in their roles by supporting your professional development.

We will provide:

• A competitive salary and bonus scheme
• Excellent staff development and training opportunities
• Corporate gym membership (and a complimentary wellness space in our London office)
• Generous pension contribution
• Free breakfast and lunch in our employee restaurant
• Private medical insurance and other benefits

Capula is committed to fostering a collaborative and inclusive environment, providing employees with the opportunity to develop their skills and advance their careers in the financial sector. We actively promote equality of opportunity for all with the right mix of talent, skills and potential, and welcome applications from a wide range of candidates.