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# Data Quality Engineer

- **Company:** Funding Circle
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Adaptable Database Systems, Artificial Intelligence, Amazon S3, Business Logic, BigQuery, Information Engineering, Data Warehousing, Database Queries, Database Testing, Dimensional Modeling, Snowflake, Data Lakes, AWS Data Analytics, Amazon Redshift
- **Published:** July 31, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=4334e4394293da67

## About the Role

* Strong SQL skills, with experience writing efficient and well structured queries
 * Hands on experience with DBT, building and maintaining modular, scalable data models that follow best practices
 * Previous experience working on Data Quality engagements
 * Strong understanding of dimensional modelling
 * Strong understanding of Data Quality and how it is measured
 * Desireable: Experience writing complex validation logic using tools like Great Expectations, dbt tests, Elementary, Soda, or Deequ.
 * Familiarity with AWS data services ( S3, Athena, Glue )
 * Experience working with data lakes or modern data warehouses ( Snowflake, Redshift, BigQuery )
 * A pragmatic problem solver who can balance technical excellence with business needs
 * Able to architect custom AI solutions and smart automated systems that are safe, maintainable, and aligned with our long-term mission
 * Act as a technical bridge and mentor, training "builders" across the company to move from basic AI usage to sophisticated system creation

## Description

London (Hybrid) | 2 days in the office | Competitive Salary + Benefits, * Building and owning high quality dbt models, ensuring they are performant, well documented and thoroughly tested

 * Rule Engineering: Partner with product engineering and business teams to translate business logic into programmatically enforced Data Quality Rules
 * Collaborate with analysts and data scientists to understand their needs and build models that drive Data Quality and the visibility and measurability of Data accuracy.
 * Champion best practices for Data Quality and data testing, across data engineering teams
 * Review and work on Data Quality issues, both from a technical perspective and performing root cause analysis with stakeholders


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