Machine Learning Data Engineer

The Role

We are hiring a hands‑on Machine Learning Data Engineer for a boutique asset management company to design, build, and scale a production‑grade, Snowflake‑centric data platform that powers analytics and machine‑learning use cases across the firm.

This is a builder role. You will own data pipelines end‑to‑end and work closely with technology, analytics, and business stakeholders to deliver reliable, well‑governed, ML‑ready datasets.

Key Responsibilities

• Design, build, and operate scalable data pipelines ingesting data from internal systems, APIs, and external providers
• Own and evolve a Snowflake‑based warehouse / lakehouse, including schema design, transformations, and optimisation
• Implement ELT processes to create trusted datasets for analytics and machine learning
• Build and maintain ML‑ready datasets and feature pipelines supporting experimentation and production models
• Support batch and near‑real‑time data workflows
• Ensure data quality, freshness, and reliability through monitoring, validation, and alerting
• Apply best practices around data governance, access control, and documentation
• Partner with analytics and business teams to translate requirements into durable data products
• Continuously improve performance, scalability, and cost efficiency

Required Experience

• 5+ years’ experience in data engineering or ML‑data engineering roles
• Strong, hands‑on Snowflake experience (production usage, not exposure)
• Advanced Python and SQL
• Proven experience owning end‑to‑end data pipelines
• Experience with data modelling for analytics and ML use cases
• Familiarity with orchestration tools (e.g. Airflow, Dagster)
• Experience working in production, high‑accountability environments
• Strong communicator, comfortable working cross‑functionally

Highly Preferred

• Experience supporting machine‑learning workflows (feature engineering, training datasets, model inputs)
• Exposure to Databricks / Spark
• Background in financial services or regulated data
• Experience in lean, execution‑focused teams

This Role Is Not

• A pure Data Scientist or ML research role
• A BI‑only or reporting‑focused data role
• A DevOps / cloud infrastructure role
• An AI‑product or agentic‑systems architect role