Job Description

Job Goals Design and implement resilient data pipelines to ensure data reliability, accuracy, and performance. Collaborate with cross-functional teams to maintain the quality of production services and smoothly integrate data processes. Oversee the implementation of common data models and data transformation pipelines, ensuring alignement to standards. Drive continuous improvement in internal data frameworks and support the hiring process for new Data Engineers. Regularly engage with collaborators to discuss considerations and manage the impact of changes. Support architects in shaping the future of the data platform and help land new capabilities into business-as-usual operations. Identify relevant emerging trends and build compelling cases for adoption, such as tool selection. Ideal Skills & Capabilities A minimum of 6 years of experience in a comparable Data Engineer position is required. Data Engineering Expertise: Proficiency in designing and implementing resilient data pipelines, ensuring data reliability, accuracy, and performance, with practical knowledge of modern cloud data technology stacks (AZURE) Technical Proficiency: Experience with Azure Data Factory and Databricks , and skilled in Python , Apache Spark , or other distributed data programming frameworks. Operational Knowledge: In-depth understanding of data concepts, data structures, modelling techniques, and provisioning data to support varying consumption needs, along with accomplished ETL/ELT engineering skills. Automation & DevOps: Experience using DevOps toolchains for managing CI/CD and an automation-first mindset in building solutions, including self-healing and fault-tolerant methods. Data Management Principles: Practical application of data management principles such as security and data privacy, with experience handling sensitive data through techniques like anonymisation/tokenisation/pseudo-anonymisation.