Senior Data Engineer

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 teams 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. Youll 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. Youll ship code that directly impacts how quickly we can test hypotheses and how confidently we can put capital behind them.

Key Responsibilities

What Youll 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: Masters 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 Youll 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.