As a Quality Engineer, you will drive quality across the entire application lifecycle by enabling full-stack solutions through multidisciplinary team planning and ecosystem integration. You will be responsible for continuous testing, including security, API, and regression suites. Developing automation strategies, creating automated scripts, and supporting data and environment configurations are key aspects of this role. You'll actively participate in code reviews, monitor performance, and report defects to foster continuous improvement in our end-to-end testing processes.

Your responsibilities will include retrieving, integrating, and preparing diverse datasets for the Data & Analytics team and the broader business user community. You will act as a key technical interface with local IT teams and business stakeholders to understand data requirements and deliver effective solutions. Furthermore, you will design, develop, and deploy robust data products and solutions within a Microsoft Azure environment, focusing on data acquisition, integration, curation, and distributed data processing. Automating scalable data pipelines using Azure Databricks and Azure Data Factory, transforming datasets for rapid consumption, and contributing to the migration of on-premise data solutions to Azure will also be part of your role. You will also provide technical expertise and mentorship to promote best practices within data engineering.

A minimum of 2-5 years of experience is required for this role. Essential skills include a strong foundation in Data Engineering, with hands-on experience in Azure Data Platform services such as Azure Data Factory, Azure Databricks, and Azure Data Lake Storage. You should possess solid experience in building modular, extensible, and resilient ETL/ELT data pipelines and be able to support BI/reporting application development using Microsoft Power BI. Experience with Microsoft Azure-based Enterprise Data Warehouse implementations, DWH/Datamart design, and data engineering for enterprise-scale BI/Reporting applications is crucial. In-depth knowledge of the Azure data ecosystem, including Azure SQL Data Warehouse (Synapse) and advanced Databricks features, is expected. Experience migrating on-premise data pipelines and complex data warehouses to Azure is highly desirable. Proficiency in data pipeline automation, orchestration, and administration using a comprehensive DevOps toolchain is also required, alongside knowledge of data modeling principles and metadata management techniques.