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
- 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
- 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