JOB DESCRIPTION:

Core-Asset Consulting is delighted to be partnering with a highly respected financial service firm to recruit a Data Engineer for a permanent position based in Newcastle. This is a fantastic opportunity to join an organisation with a clear commitment to innovation and insight-led decision-making.
As a Data Engineer, you will play a critical role in transforming complex operational data into meaningful insights. Working across analytics, data science, and development teams, you will design and implement robust data pipelines, optimise data models, and ensure high-quality, reliable outputs that drive strategic business decisions.

ESSENTIAL SKILLS/EXPERIENCE:

• Strong proficiency in T-SQL and experience developing complex queries and procedures.
• Expertise in Spark and PySpark for scalable data processing.
• Solid understanding of OLTP and OLAP architectures and data modelling principles.
• Experience with Microsoft Azure, including Fabric, Azure Data Factory, Data Lake, Data Warehouse, and Blob Storage.
• Sound knowledge of ETL/ELT best practices.
• Strong analytical and problem-solving skills with a focus on data accuracy and reliability.
• Excellent collaboration and communication skills, with the ability to translate business requirements into technical solutions.
• Comfortable working in dynamic environments and committed to continuous improvement.

• Translate complex transactional data into optimised analytical models (facts and dimensions) to support reporting and insights.
• Design, build, and enhance data pipelines within Microsoft Fabric to improve performance, scalability, and data quality.
• Engineer new integrations from internal and external data sources using best-practice ETL/ELT methodologies.
• Monitor and optimise SQL-based workflows to ensure accuracy and efficiency in daily data processing.
• Lead efforts to migrate legacy reporting datasets to modern platforms, ensuring minimal disruption.
• Collaborate with analytics teams to develop custom data models and engineered datasets tailored to business requirements.
• Partner with data science colleagues to create summary and aggregated datasets for advanced analytics and predictive modelling.
• Respond efficiently to ad hoc data requests, ensuring outputs are timely, accurate, and aligned to stakeholder needs.