Senior Data Engineer Global Credit Hedge Fund (Boston)

My client is global investment manager with a strong track record of success across public and private credit markets. Their strategies span investment grade, high yield, leveraged loans, and structured products, with billions in AUM and a reputation for innovation. Technology and data are at the heart of how they invest and manage risk.

They are seeking a senior Data Engineer to join their growing Technology & Analytics team in Boston. This is a greenfield opportunity to design and build the next generation of their data platform, working closely with senior portfolio managers, traders, risk managers, and operations leaders.

As a Data Engineer, you will be central to building scalable, high performance data infrastructure and pipelines that drive investment decisions and reporting across the business. You will have real ownership, visibility with the C-suite, and the opportunity to make a tangible impact on a global scale.

Key Responsibilities

1. Build and own data pipelines and ETL workflows to support trading, risk, operations, and finance teams.
2. Design and develop a modern data architecture for structured and unstructured data, enabling real-time analytics and reporting.
3. Partner directly with senior business stakeholders to understand requirements and deliver practical, high-value solutions.
4. Enhance and optimize the firm’s existing data ecosystem, ensuring scalability, resilience, and accuracy.
5. Integrate third-party and market data sources (loans, bonds, CLOs, derivatives) into the central data platform.
6. Develop monitoring and data quality tools to ensure integrity and reliability of mission-critical data.
7. Continuously innovate, evaluating new technologies to drive automation, analytics, and decision-making.

Requirements

1. Strong academic background in Computer Science, Engineering, or a related field.
2. 3–7 years of experience as a Data Engineer, preferably within financial services, fintech, or hedge funds.
3. Expertise in SQL, Python, and modern data engineering frameworks.
4. Hands-on experience with data modeling, warehousing, and cloud technologies (Azure, AWS, Snowflake, or similar).
5. Familiarity with financial products (credit, loans, bonds, CLOs, derivatives) is a strong plus.
6. Experience building real-time data pipelines and analytics solutions.
7. Excellent communication skills, with the ability to translate complex technical solutions into business impact.