Python Engineer

Location: Remote

Contract

I am currently working with a leading hedge fund that is looking for a strong Python Engineer to join their growing technology team. The role focuses on building robust data and analytics tools that support investment, trading, and risk functions across the firm, with particular emphasis on fixed income and credit markets.

Role Overview

You will be responsible for developing and enhancing Python-based systems used across the investment lifecycle. The ideal candidate will have strong Python skills, solid experience working with financial markets data, and good knowledge of fixed income products such as bonds, rates, or credit instruments.

This is a hands-on engineering role with a strong data focus, working closely with front-office and technology stakeholders.

Key Responsibilities

Develop and maintain Python applications and internal tools used by investment and risk teams

Build reliable ETL/ELT pipelines for financial, market, and operational datasets

Work with structured financial data, market data feeds, and fixed income datasets

Collaborate with traders, analysts, and technologists to understand workflow needs

Integrate pricing, analytics, or risk libraries into internal systems

Create automation and scripting tools to improve efficiency

Implement best practices in testing, documentation, version control, and deployment

Support the enhancement of data quality, reliability, and performance across systems

Required Skills & Experience

Essential:

Strong hands-on Python programming experience

Background in investment banking, hedge funds, or financial markets

Good understanding of fixed income (bonds, credit, rates)

Experience building ETL/ELT pipelines and handling large datasets

Strong SQL skills and familiarity with structured data environments

Experience working with trading, risk, or analytics systems

Strong communication and ability to work closely with front-office and data teams

Desirable:

Experience with fixed-income analytics or pricing libraries

Exposure to credit products, market data feeds, or time-series data

Familiarity with cloud technologies or distributed data systems

Experience with automation, scripting, and workflow optimisation

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