5 of 5 Permanent ETL Jobs in Leeds

work as part of a collaborative team to solve problems and assist other colleagues. - Ability to learn new technologies, programs and procedures. Technical Essentials: - Expertise across data warehouse and ETL/ELT development in AWS preferred with experience in the following: - Strong experience in some of the AWS services like Redshift, Lambda,S3,Step Functions, Batch, Cloud formation, Lake Formation More ❯

lakehouse. Develop semantic models and dimensional structures optimised for BI, dashboarding, and machine learning. Ensure documentation, governance, and data consistency across domains. Collaborate with data engineers to support robust ETL/ELT pipelines and maintain end-to-end data lineage. Deploy analytics engineering solutions using dbt, SQL transformations, and CI/CD best practice. Partner with analysts and data scientists More ❯

Lambda). Proficient in Python, Apache Spark, and SQL. Experience in data warehouse design and data migration projects. Cloud data platform development and deployment. Expertise across data warehouse and ETL/ELT development in AWS preferred Experience is building a Data Lake as well as building CI/CD data pipelines Excellent communication and problem-solving skills. What's on More ❯

Lead initiatives to improve data quality , including profiling, cleansing, monitoring, and remediation strategies. Provide architectural guidance on MDM platform selection , cloud vs. on-prem deployment , and integration patterns (API, ETL, messaging). Support metadata management , lineage tracking , and compliance initiatives. Excellent communication and stakeholder management skills. Experience in multi-domain MDM (Customer, Supplier, Location, etc.) is a strong plus. Knowledge More ❯

throughout delivery. As a Data Engineer, you will: Digest data requirements, gather and analyse large scale structured data and validates by profiling in a data environment Design and develop ETL patterns/mechanisms to ingest, analyse, validate, normalize and clean data Implement data quality procedures on data sources and preparation to visualize data and synthesize insights for business value Support More ❯