Job Description

Senior Data Engineer ML Platform

A globally renowned systematic hedge fund is seeking a Senior Data Engineer to join their Machine Learning Platform Engineering team. This is a high-impact role at the intersection of data, infrastructure, and machine learning, supporting the next generation of quantitative research and trading strategies.

As a senior engineer, you will build scalable, production grade data pipelines and develop robust infrastructure powering ML experimentation, model training, and inference across the firm. This team owns the end-to-end machine learning platform used by quantitative researchers, data scientists, and traders.

Key Responsibilities:

• Design and implement scalable, fault-tolerant data pipelines supporting ML workflows—from feature engineering and labelling to training and evaluation.
• Build internal platforms and services for model orchestration, experiment tracking, dataset versioning, and automated deployment of ML models.
• Partner with data scientists and quant researchers to understand workflow requirements and deliver performant, reusable tooling and infrastructure.
• Oversee best practices in data engineering and ML Ops across ingestion, validation, transformation, and lineage tracking.
• Own monitoring, observability, and operational support for production data and ML pipelines.

Ideal Candidate Profile:

• Extensive experience in Python (or Java/Scala), with strong system design and data engineering skills.
• Hands-on experience with modern ML Ops tooling, such as Airflow, Argo Workflows, Kubeflow, or similar.
• Familiarity with distributed compute and storage systems such as Spark, Hive, Snowflake, or Delta Lake.
• Knowledge of containerization and orchestration (Docker, Kubernetes) in a production ML or data platform setting.
• Deep understanding of data modeling, data versioning, and best practices in building resilient, testable pipelines.
• Proven ability to work across technical and non-technical teams, especially in research or front-office environments.
• Prior exposure to financial data and quantitative research is a strong plus but not required.

This is an opportunity to shape the future of ML infrastructure within a world-class quantitative investment firm. Apply now for a confidential discussion.

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