Front Office Engineer

This is a front-office engineering role performed on behalf of clients. You will build and operate systems used daily by portfolio managers, traders, research analysts, and operations teams at sophisticated hedge funds, working directly with those teams to refine and deliver solutions. The work is often ambiguous rather than fully specified, and you will be expected to turn questions or issues into production systems by ingesting data, modeling it correctly, computing analytics, exposing it through APIs, and standing behind the results. Correctness and speed both matter, as the output supports trading, risk, and reporting decisions. You will build on shared core infrastructure such as APIs, data pipelines, and analytics engines, focusing on backend and analytical systems rather than UI ownership.

Responsibilities:

• Turn ambiguous requests from client investment and operations staff into specifications and production systems.
• Design and maintain high-performance APIs using Python and frameworks such as FastAPI.
• Build and operate reliable ingestion and transformation pipelines for market, position, and reference data using orchestration tools such as Airflow, Dagster, or Prefect.
• Architect analytical and transactional data models that support evolving business needs.
• Implement analytics layers for performance, exposure, and risk calculations using linear algebra and time-series operations with tools such as Pandas or Polars.
• Build reconciliation, validation, and monitoring processes to ensure data quality.
• Maintain metric definitions and a semantic layer so metrics remain consistent across dashboards, reports, and AI agents.
• Trace discrepancies through pipelines back to source data and debug numerical issues, not just application errors.
• Work directly with client investment, research, and operations teams, alongside engineers across distributed teams, to gather requirements, iterate, and support live workflows.

Requirements:

• 3-10 years of engineering experience, with at least 2 years in or adjacent to financial markets.
• Working knowledge of investment data models, including positions, securities, transactions, P& L, exposure, and returns, and how front-office teams use them.
• Direct experience at a hedge fund, asset manager, proprietary trading desk, sell-side desk, fund administrator, or a vendor serving these clients.
• Degree in computer science, engineering, mathematics, or a related quantitative field, or equivalent practical experience.
• Strong Python and SQL skills.
• Hands-on experience building APIs using FastAPI, Flask, or Django, with a solid understanding of RESTful and secure API design.
• Production experience with data pipelines and orchestration tools such as Airflow, Dagster, or Prefect.
• Experience with analytical databases and data warehouses such as Snowflake, ClickHouse, DuckDB, SQL Server, or Databricks, along with deliberate data modeling.
• Experience with analytical libraries such as Polars and Pandas for computationally heavy workloads.
• Familiarity with data quality frameworks and monitoring.
• Hands-on experience with Docker and containerized development.
• Exposure to cloud platforms such as Azure, AWS, or GCP and cloud-native architectures.
• Comfort with ambiguity and the judgment to ask the right questions of non-technical stakeholders.
• Excellent communication skills for translating technical concepts to investment and operations staff.

Nice To Have:

• Experience with portfolio analytics, risk platforms, or fund accounting systems.
• Comfort with complex models such as factor risk, attribution, or reconciliation.
• Familiarity with AI development workflows and agentic frameworks such as Claude Code, LangGraph, MCP servers, or RAG.
• Familiarity with event-driven and streaming architectures for real-time and batch data processing.
• Hands-on exposure to dbt and local analytics with DuckDB.
• Experience optimizing performance on large time-series workloads.
• Track record of shipping in fast-paced, client-facing environments.
• Ability to read frontend code and collaborate effectively with UI teams.