Lead Backend Engineer / Developer (Python + Java + Cloud) - Manager

Role

Lead Backend Engineer (Manager) responsible for backend service development, data modeling, time-series analysis, and building end-to-end infrastructure for data gathering, cleaning, signal generation and model implementation in a financial services environment.

Key Responsibilities

• Design, implement and deploy scalable enterprise web applications and RESTful APIs using microservices.
• Lead data modeling, develop analytical data models, perform time-series data analysis and data anomaly detection.
• Implement backend computation pipelines and automate advanced data processing for standalone execution in controlled environments.
• Build end-to-end infrastructure for data gathering, cleaning, signal generation and model implementation.
• Work with global teams on design, release engineering, deployments and support following company standards.
• Troubleshoot production issues, perform root cause analysis and maintain operational stability.
• Communicate project plans and technical details with counterparties across regions and collaborate with other teams to improve solutions.

Requirements

• 10–14 years of IT experience; leadership/lead engineer experience expected.
• Strong programming and design skills in Python and Java; expert understanding of Python core concepts, functional and class-based design.
• 5+ years of Python backend development experience; demonstrable experience with common Python packages such as NumPy, Pandas, matplotlib, SAlib, Scikit-learn.
• Experience implementing REST APIs and microservices; experience with cloud platforms (AWS or Azure) and on-prem environments.
• Experience with RDBMS such as Oracle, SQL Server, and Postgres; familiarity with Databricks.
• Strong quantitative and mathematical background; experience or familiarity with finance domain and statistical concepts preferred.
• Experience with distributed Agile and Lean practices, release engineering, and managing large datasets and computationally intensive tasks.

Good to Have

• MLOps and quantitative mathematical model development experience across multi-asset data.
• Experience in quantitative financial research, market data analysis, and familiarity with vendor pricing/fundamentals datasets.
• 4–5 years of buy-side or sell-side research experience is desirable.

Work Schedule & Location

• Work schedule: Hybrid
• Primary team location: Cambridge (just off Harvard Square), USA; business/technology teams also present in India, UK, Poland, China.

Notes

• Must be able to manage multiple projects/tasks and deadlines while maintaining attention to detail.
• Expected to establish processes for working with very large datasets and optimize repetitive/computationally intensive tasks.