Senior Front Office Data Analytics Engineer

Do you have the right skills and experience for this role Read on to find out, and make your application.

Overview

• You will work directly with the Front Office investment and trading teams to build, enhance, and support data and analytics infrastructure that powers research, trading, and portfolio decision-making.
• This is a hands-on, high-impact role at the core of our front office. You will be expected to take full ownership of your work—from design through production—and to operate with a self-starter mindset in a fast-paced, collaborative environment.

Essential skills & experience:

• You should have at least 5+ years' experience as a Front Office Engineer (buy-side, sell-side, or trading environment).
• Offer deep expertise in Python, with strong software engineering practices (version control, testing, CI/CD).
• Have a proven track record of building robust data pipelines in cloud-native environments (preferably AWS).
• Experience with Docker and container-based deployments.
• Strong knowledge of Snowflake and NoSQL databases (especially MongoDB).
• Solid understanding of financial markets and instruments particularly Fixed Income (or may consider with an exposure to credit, rates, equities, options, etc.
• Experience with market data providers (Bloomberg, Refinitiv, etc.) would be useful
• Any familiarity with tools such as Airflow, prefect, or other orchestration frameworks would be advantageous.
• Experience building internal tools or dashboards using Dash, Streamlit, or similar web-based data analytics platforms would be nice to have

Key Responsibilities

• Work closely with portfolio managers, analysts, and traders to understand data and research requirements and build scalable solutions.
• Design, implement, and maintain real-time and batch data pipelines across internal and external sources.
• Manage and optimize data workflows on AWS, including containerized environments using Docker.
• Ingest, transform, and serve large-scale financial datasets across asset classes using Python, Snowflake, and NoSQL databases (e.g., MongoDB).
• Ensure data quality, integrity, and availability across the front office stack.