We are looking for an experienced Data Engineer to join a growing data and AI team, helping to build modern data solutions that support advanced analytics and machine learning.

This role centres on building robust data ingestion pipelines within Snowflake and delivering predictive analytics using Snowflake Cortex. While the majority of machine learning will leverage Snowflake's managed ML capabilities, there will also be opportunities to develop bespoke Python models where business requirements extend beyond native functionality.

You will play a key role in designing scalable data engineering solutions, preparing high-quality datasets, and ensuring production-ready machine learning outputs are delivered to downstream applications.

Key Responsibilities
  • Design, build and maintain scalable data ingestion pipelines into Snowflake from structured and time-series data sources.
  • Develop predictive analytics solutions using Snowflake Cortex, including forecasting, anomaly detection and classification.
  • Prepare machine learning datasets through feature engineering, windowed aggregations and time-series transformations.
  • Develop custom machine learning models using Python and Snowpark (including scikit-learn, XGBoost and LightGBM) where native Snowflake capabilities are not suitable.
  • Integrate machine learning outputs into downstream applications and analytics platforms.
  • Monitor and maintain production data pipelines and machine learning solutions, including data quality, model refreshes and performance monitoring.
  • Optimise Snowflake performance and ensure data engineering best practices are followed.
Required Skills & Experience
  • Strong commercial experience with Snowflake, including SQL, Streams, Tasks, Snowpipe and performance optimisation.
  • Experience using Snowflake Cortex ML capabilities for forecasting, anomaly detection and classification.
  • Strong Python development skills, including Snowpark.
  • Experience building data pipelines and preparing datasets for machine learning.
  • Knowledge of time-series data processing and feature engineering techniques.
  • Experience developing machine learning models using libraries such as scikit-learn, XGBoost or LightGBM.
  • Understanding of production monitoring, data quality and model lifecycle management.
Desirable Experience
  • Experience with Snowflake ML features such as Model Registry and Feature Store.
  • Knowledge of Azure Data Lake, Microsoft Fabric or other modern cloud data platforms.
  • Familiarity with Snowflake AI capabilities such as AISQL, Cortex Search or related technologies.