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# Snowflake Data Engineer - Remote

- **Company:** UST
- **Location:** UK (Remote available)
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, Cluster Analysis, Computer Programming, Data Architecture, Extract Transform Load (ETL), Data Security, Data Warehousing, Dimensional Modeling, Python (Programming Language), Meta-Data Management, Performance Tuning, Query Optimization, Power BI, Tableau (Software), Workflow Management Systems, Data Ingestion, Sql Optimization, Snowflake, Data Management, Looker Analytics, Data Pipelines
- **Published:** June 29, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=c6a18f32b3d9b5dc

## About the Role

Do you have experience in Tableau?, * Strong hands-on experience with Snowflake (data modeling, SQL optimization, performance tuning)

 * Advanced SQL skills (analytical queries, window functions, complex joins)
 * Strong experience in data warehousing concepts
 * Experience with ETL / ELT frameworks
 * Programming experience in Python (or equivalent)
 * Experience with cloud platforms (AWS / Azure / GCP)
 * Solid understanding of data architecture, dimensional modeling, and metadata management

Good to Have

 * Experience with orchestration tools (Airflow, ADF, Glue, etc.)
 * Knowledge of CI/CD pipelines for data platforms
 * Exposure to reporting/BI tools (Power BI, Tableau, Looker, etc.)
 * Experience in financial services / asset management domain

## Description

The role involves working closely with architects, data analysts, and downstream consumers to deliver high-quality, performant, and secure data pipelines supporting enterprise analytics and reporting., * Design and develop robust data models and scalable data pipelines using Snowflake

 * Implement ELT/ETL pipelines using Snowflake features such as Streams, Tasks, and Snowpipe
 * Optimize Snowflake performance through clustering, query tuning, and cost optimization
 * Build and maintain data ingestion pipelines from multiple sources (batch and near-real-time)
 * Ensure data quality, integrity, and governance across platforms
 * Collaborate with business stakeholders, architects, and downstream teams
 * Support production issues, root-cause analysis, and performance improvements
 * Adhere to security, compliance, and data privacy standards


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