Analytics Engineer

Responsibilities: Build and maintain analytics, calculations, and models covering portfolio monitoring, risk, and performance. Engineer data pipelines and data models in Python, SQL, and Spark-based platforms. Deliver dashboards, APIs, and AI-powered tools that let investment users explore data and run scenarios. Meet directly with portfolio managers and analysts to gather requirements and validate prototypes. Help newly launched investment teams get the data and tooling they need from day one. Partner with data engineering, market data, governance, and platform teams to keep solutions reliable and secure in production.

Required Experience: Roughly 3 to 5 years as a software, data, or analytics engineer, ideally supporting front-office users in financial services. Strong Python data skills (Pandas, NumPy, notebooks, plotting libraries) and advanced SQL with relational modeling. Exposure to a distributed data platform such as Spark, Databricks, or Snowflake. Working knowledge of at least one asset class, such as equities, credit, or fixed income. Experience shipping user-facing analytics through a BI tool or Python web framework, plus familiarity with AI-assisted development.