ML Data Engineer - Contract-to-Hire

The role:

We are looking for a hands‑on Machine Learning Data Engineer to design, build, and scale a production‑grade data platform that supports analytics and machine learning use cases across the firm.

This is a builder role, not a maintenance role. You will own data pipelines end‑to‑end from ingestion through to ML‑ready datasets and work closely with Technology, Analytics, and business stakeholders to deliver reliable, scalable data solutions.

What You'll Be Responsible For

• Design, build, and maintain scalable, production‑ready data pipelines ingesting data from APIs, SaaS platforms, databases, and files
• Own and evolve a centralised data lake / lakehouse, curating trusted datasets for analytics, reporting, and machine learning
• Develop robust ELT/ETL workflows to clean, validate, and model data into consumable, well‑governed tables
• Build and maintain ML‑ready datasets and feature pipelines to support experimentation and production models
• Work extensively with Snowflake, including schema design, transformations, performance optimisation, and downstream usage
• Ensure data quality, freshness, and reliability through monitoring, alerting, and automated validation
• Partner with analytics and business teams to define data requirements, metrics, and data products
• Support downstream consumers, including BI tools, dashboards, operational reporting, and external data exports
• Apply best practices around data governance, security, access control, and documentation
• Continuously improve performance, scalability, and cost efficiency of the data platform

Required Experience & Skills

• 4+ years of hands‑on data engineering experience
• Strong proficiency in Python and SQL (used daily in production)
• Practical Snowflake experience
• Proven experience building and operating modern data architectures (lakehouse, data lake, or warehouse)
• Experience designing data models and schemas for analytics and ML use cases
• Hands‑on experience with workflow orchestration tools (e.g. Airflow, Dagster, Prefect)
• Strong understanding of data quality, testing, and monitoring in production systems
• Comfortable integrating data from APIs and third‑party systems
• Ability to operate independently in a fast‑paced, high‑accountability environment
• Strong communication skills and confidence working with technical and non‑technical stakeholders

Strongly Preferred

• Direct experience supporting machine‑learning workflows (feature engineering, training datasets, ML pipelines)
• Experience with cloud platforms (AWS, Azure, or GCP)
• Exposure to Databricks / Spark and large‑scale data processing
• Experience with streaming or near real‑time pipelines (e.g. Kafka)
• Background in financial services, fintech, or regulated data environments
• Experience working in high‑growth, execution‑focused teams

Culture Fit (Non‑Negotiable)

• Self‑starter who takes full ownership of problems and solutions
• Comfortable being "thrown into the deep end"
• Bias toward action and results over process
• Not looking for a slow, heavily layered organisation