Data Engineer
Senior Data Engineer at Newbridge responsible for building and maintaining scalable data pipelines that support quantitative research and live strategy deployment, leveraging Python, SQL, Spark, and AWS to deliver high‑quality, reliable data across the research lifecycle.
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
Posted June 18, 2026