Data Engineer: Scalable Pipelines on Azure Fabric

Core-Asset Consulting; Newcastle upon Tyne; On-site; GBP 55,000 - 75,000; Full time

Benefits offered by this job

Competitive salary

Wide benefits package

Job summary

Core-Asset Consulting is seeking a data engineer to design, build and enhance data pipelines using Microsoft Fabric. You will collaborate with business teams to deliver robust data solutions and ensure data quality across analytics projects.

The role emphasizes translating transactional data into scalable models, and integrating diverse data sources with best-practice ETL/ELT methods while supporting ongoing reporting needs.

Qualifications

• Strong proficiency in T-SQL and complex queries/ stored procedures.
• ,

Responsibilities

• Translate complex transactional data into optimised analytical models (facts and dimensions).
• Design, build, and enhance data pipelines within Microsoft Fabric to improve performance and data quality.
• Engineer integrations from internal and external data sources using best-practice ETL/ELT.
• Monitor and optimise SQL-based workflows for accuracy and efficiency in daily processing.
• Lead migration of legacy reporting datasets to modern platforms with minimal disruption.
• Collaborate with analytics teams to develop custom data models and engineered datasets.
• Partner with data science colleagues to create summaries and aggregated datasets for analytics.
• Respond to ad hoc data requests with timely, accurate outputs.

Skills

T-SQL

Spark

PySpark

OLTP/OLAP

Microsoft Fabric

Azure Data Factory

ETL/ELT

Data modelling

Data quality

Analytical thinking

Collaboration

Tools

Microsoft Fabric

Azure Data Factory

Data Lake

Data Warehouse

Blob Storage

Job description

Core-Asset Consulting is seeking a data engineer to design, build and enhance data pipelines using Microsoft Fabric. You will collaborate with business teams to deliver robust data solutions and ensure data quality across analytics projects.

The role emphasizes translating transactional data into scalable models, and integrating diverse data sources with best-practice ETL/ELT methods while supporting ongoing reporting needs.