Our clients Research & Development team brings together specialists across disciplines to push the frontier of quantitative investing. They blend deep expertise with novel ideas to design and deploy new investment strategies, backed by rigorous research and engineering. The team's core mission is to accelerate world-class research by delivering high-quality, reliable data and the scalable technology systems that make it accessible.
Role Overview
As a Data Engineer in R&D, you will own critical pieces of the data backbone that powers our entire research lifecycle — from idea generation to live strategy deployment. You'll work side-by-side with quantitative researchers, software engineers, and portfolio managers to onboard novel datasets, extract signal, and ensure our infrastructure is fast, reliable, and researcher-friendly.
This is a high-ownership role. You'll ship code that directly impacts how quickly we can test hypotheses and how confidently we can put capital behind them.
Key Responsibilities
What You'll Do
Data Ingestion & Cleansing
Design, build, and operate batch and real-time pipelines to ingest, cleanse, normalize, tag, and integrate diverse new data sources — structured, semi-structured, and unstructured.
Data Modeling & Quality
Define canonical data models and schemas. Implement automated data quality checks, lineage tracking, and monitoring to ensure data is accurate, timely, and well-documented.
Exploratory Analysis
Profile new datasets: generate descriptive statistics, identify anomalies, assess signal content, and demo potential applications to researchers.
Infrastructure & Platform
Architect and maintain scalable systems for data storage, transformation, feature generation, and low-latency retrieval. Optimize for cost, performance, and reliability.
Tooling & Enablement
Build internal tools, APIs, and self-serve frameworks that make it easy for researchers to discover, access, and use data without engineering bottlenecks.
Collaboration
Partner with quants to understand research needs and translate them into data products. Work with core infra to ensure systems are production-grade and compliant.
Minimum Qualifications
- Education: Master's or Ph.D. in Computer Science, Mathematics, Statistics, Physics, Engineering, or another quantitative STEM discipline. Equivalent hands-on experience considered.
- Experience: Professional experience in data engineering, software engineering, or data science with a heavy engineering component.
- Technical Skills: Expert-level Python and SQL. Strong proficiency in at least one additional language such as C++, Java, Scala, or Go.
- Data Systems: Deep experience with modern data stack: distributed storage/compute like Spark, distributed file systems, columnar databases, and workflow orchestration like Airflow/Dagster.
- Analytical Skills: Demonstrated ability to apply statistical and computational methods to large, messy, real-world datasets to drive decisions.
- Problem Solving: Track record of decomposing complex, ambiguous problems and delivering pragmatic, high-quality solutions.
- Communication: Ability to explain technical trade-offs and data insights clearly to both technical and non-technical stakeholders.
Preferred Qualifications
- Cloud & DevOps: Hands-on experience with AWS, GCP, or Azure, plus containerization, IaC, and CI/CD best practices.
- Domain Data: Familiarity with financial market data, alternative data, tick data, or other high-frequency time-series datasets.
- Performance: Experience optimizing for low-latency data access, high-throughput ETL, or large-scale numerical computation.
- ML Infra: Exposure to feature stores, model training pipelines, or supporting ML research workflows.
- No finance background required: We hire exceptional engineers and scientists and teach them investing.
What You'll Find Here
- Impact: Your work directly enables new research and live strategies. Short feedback loops from code to P&L.
- Colleagues: A collegial, multidisciplinary team where deep experts teach and learn from each other every day.
- Complexity: Hard, unsolved problems at the intersection of data, systems, and markets. No two days look the same.
- Autonomy: High ownership, minimal bureaucracy. We trust you to identify problems and ship solutions.
- Growth: Continuous learning via research seminars, code reviews, and exposure to the full quant research stack.
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