Data Engineer jobs in United Kingdom

• Pipeline Architecture & Development: Design, build, and maintain scalable, reliable, and high-performance data pipelines using Azure Data Factory, Databricks, and Spark to support machine learning and analytics workloads.
• ETL & Data Integration: Lead the creation and optimization of ETL processes for structured and unstructured data, ensuring data quality, lineage, and compliance with enterprise standards.
• Feature Store Management: Develop and manage feature stores to streamline the reusability and governance of engineered features for AI and ML models.
• ML Ops and Automation: Implement robust MLOps workflows that enable continuous integration, deployment, monitoring, and retraining of ML models in production environments.
• Collaboration: Partner with data scientists, ML engineers, and business stakeholders to translate analytic requirements into scalable data solutions, and provide guidance on best practices for data engineering in an AI context.
• Communication: Excellent communication skills to collaborate effectively with cross-functional teams, including data scientists, analysts, and business stakeholders.
• Cloud and Security: Ensure secure data movement and storage by applying security best practices and compliance protocols within Azure cloud environments.
• Continuous Improvement: Stay current with advancements in data engineering, MLOps, and cloud technologies, and proactively apply new techniques to enhance existing solutions.

• Education: Bachelor’s or Master’s degree in Computer Science, Information Management, Data Engineering, or a closely related technical field.
• Experience: 4-6 years of professional experience in data engineering, with a strong emphasis on ML Ops, data pipelines, and large-scale ETL/ELT processes.
• Technical Skills: Advanced proficiency in Azure Data Factory, Databricks, and Apache Spark for designing and implementing complex data pipelines and distributed data processing jobs; expertise in ETL/ELT processes, data wrangling, and integrating diverse data sources into unified, high-quality datasets suitable for AI and analytics applications.
• MLOps and Workflow Management: Hands-on experience with feature store management and supporting ML and AI workflows in production environments; solid understanding of MLOps principles, including CI/CD for machine learning, automation of model deployment, and monitoring practices.
• Programming and Scripting: Proficient in SQL and Python for data transformation, orchestration, and automation tasks; familiarity with additional programming languages (e.g., Scala, R) is a plus.
• Cloud Technologies: Experience with cloud-native tools and services (e.g., Azure, AWS, Google Cloud) for data storage, processing, and orchestration.
• Data Security and Compliance: Working knowledge of data security, privacy, and compliance regulations (e.g., GDPR, HIPAA) in cloud-based solutions, ensuring data governance and protection.
• Analytical and Problem-Solving Skills: Strong analytical skills to troubleshoot data-related issues and optimize data workflows for performance and efficiency.

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