Snowflake Data Engineer

Snowflake Data Engineer

3 month Fixed Term Contract

London – Hybrid (Onsite 2 days per week)

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.

Please visit Fractal | Intelligence for Imagination for more information about Fractal

Role / Responsibilities:

• Must have Snowflake & / Snowflake ML experience.
• Fractal Data Engineer roles required with the necessary Snowflake experience.
• Data Build Tool (DBT) experience would be advantageous.
• To manage the build of nominated Programme Use Cases within Client Snowflake (Pre-Prod) during September 2026 through to December 2026 inclusive.
• Note : Exclusively for Programme Use Cases, the Fractal Engineers aligned to this area will be required to support the Data Scientists to run a Proof of Concept (PoC) Migration Phase.
• Support in transforming Programme PySpark for the respective use cases, as part of migration preparation.
• Work collaboratively with nominated Data Scientists (Programme) to validate the functionality / performance of the models.
• Document the performance, findings & observations, once the models have been built within Snowflake.
• Support the cutover of Snowflake built models from Pre-Prod to Prod, in collaboration with Data Scientists.
• Provide support during the Warranty Period for issue tracking / resolution.

Role / Skills of the Snowflake Data Engineer for Programme Migrations

1. Ideally competent to build data models / data pipelines with Snowflake ML, with input from the respective Data Scientists.
2. Be able to leverage Snowflake frameworks & workflows to construct scalable models.
3. Work collaboratively with the Data Scientists to ensure the newly built models in Snowflake function & perform correctly (when compared to the existing Programme use cases)
4. It is important the resources work collaboratively with the nominated Data Scientists to help with model / data pipeline build standardisation, issue resolution, etc.
5. PySpark experience. The pipeline is written in PySpark, it is vital that the engineers can quickly get up to speed on the pipeline, advise on efficiency improvements & best practise.
6. SQL expertise.
7. Snowflake experience alongside PySpark, so that they can advise if a better Snowflake-native solution exists - we are not tied to PySpark and are happy if there is a better solution for the pipeline.