Java, Spring Framework (Spring Boot, Spring MVC, Spring Security), Hibernate/JPA. Hands-on experience with RESTful APIs, Microservices architecture, and messaging systems (Kafka, RabbitMQ, etc.). Proficiency in RDBMS (Oracle, MySQL, PostgreSQL) and SQL. Exposure to cloud platforms (AWS, Azure, GCP) is a plus. Familiarity with CI/CD pipelines, DevOps tools (Jenkins, Git, Docker, Kubernetes). Solid understanding More ❯
Strong experience in data modeling concepts (conceptual, logical, physical, dimensional, OLTP & OLAP). Proficiency in modeling tools such as ERwin, ER/Studio, PowerDesigner, or similar. Solid knowledge of RDBMS (Oracle, SQL Server, DB2, PostgreSQL, etc.) and familiarity with NoSQL databases. Good understanding of data warehousing, ETL processes, and BI tools. Hands-on experience with SQL and performance tuning. Strong More ❯
modern data warehouse technologies - Databricks a must! Experience in data management disciplines, including data integration, modeling, optimisation, data quality and Master Data Management. Experience with database technologies such as RDBMS (SQL Server, Oracle) or NoSQL (MongoDB). Knowledge in Apache technologies such as Spark, Kafka and Airflow to build scalable and efficient data pipelines. Have worked on migration projects and More ❯
Employment Type: Contract
Rate: £700 - £750/day £700-750 Per Day (Inside IR35)
modern data warehouse technologies - Databricks a must! Experience in data management disciplines, including data integration, modeling, optimisation, data quality and Master Data Management. Experience with database technologies such as RDBMS (SQL Server, Oracle) or NoSQL (MongoDB). Knowledge in Apache technologies such as Spark, Kafka and Airflow to build scalable and efficient data pipelines. Have worked on migration projects and More ❯
enterprise-grade data models across warehouses, lakes, and vaults. The Role You'll be responsible for: Designing and developing conceptual, logical, and physical data models . Implementing models across RDBMS, ODS, data marts, and data lakes (SQL/NoSQL). Translating business needs into robust, long-term data models. Applying Data Vault 2.0 techniques in real-world implementations. Supporting metadata More ❯
