Data Engineer, Azure Data Platform

- Advanced proficiency in Python and PySpark, writing clean, modular, object-oriented code for data transformations - Strong command of SQL (T-SQL, Spark SQL) for data exploration, validation, and final-stage modelling - Deep hands-on experience with Microsoft Fabric and its tooling such as Azure Data Factory (ADF), and Azure Data Lake Storage (ADLS Gen2) - Practical experience with Git, branching strategies, automated testing (e.g. pytest), and CI/CD orchestration via Azure DevOps - Proven commercial track record of deploying complex data solutions on the Microsoft Azure platform - Experience collaborating with a range of stakeholders to structure data for downstream consumption (e.g. MLflow, Power BI semantic models) - Infrastructure-as-code experience with Terraform for Azure resource provisioning - Familiarity with streaming data architectures (Spark Structured Streaming) - Knowledge of complementary modern data stack tools such as dbt for SQL-based transformations - Experience integrating Large Language Models (LLMs) or operationalising AI/ML models - Exceptional problem-solving abilities and a persistent, detail-oriented approach to debugging complex code - Strong communication skills to effectively translate business requirements into technical architectures - A proactive mindset focused on continuous learning and staying ahead of the rapidly evolving data landscape - Willingness to review code submissions, enforce coding standards, and mentor junior engineers on the team - 3–5+ years in software engineering, data engineering, or Big Data environments with a code-first approach - Proven commercial experience deploying and maintaining complex data solutions on Microsoft Azure - Experience working in cross-functional teams