** Senior Data Engineer - Hybrid/Remote - Salary £55-65K **

This is an exceptional opportunity for a hands-on, talented data engineer who wants to lead the design and build out of a new data platform for reporting and analytics within a fast-growing Insurtech business.

The successful applicant will be a skilled data professional that can lead on building out a new companywide data platform, reporting and analytics solutions. The successful applicant will have strong experience in data modeling, data engineering and visualization for business analytics and reporting. The successful applicant will be a highly motivated and self-directed individual with excellent communication and teamwork skills. They will also have a proven track record of delivering data engineering and business intelligence solutions.

Experience

Minimum 6+ years commercial experience in a data engineering role

Have strong data modelling experience (star schema / Kimball), SQL and Python experience

Have a proven track record in data modelling, building out data infrastructure and ETL pipelines

Strong data visualisation experience in Power BI using DAX

Be experienced using Azure data platform tools including Azure Data Lake, Azure Data Factory, Synapse / Fabric, Databricks, Spark and other data tools

Advantageous to have:

Insurance domain knowledge a plus

Knowledge of machine learning and advanced analytics within the Azure ecosystem (e.g. Azure ML).

Familiarity with Data Governance practices in conjunction with Azure Purview (data lineage, data categorization, and data certification)

Certification such as Microsoft Certified: Azure Data Engineer Associate or Microsoft Certified: Azure Solutions Architect Expert.

Key Responsibilities:

Design, build and maintain dimensional and relational models based on the business requirements.

Design data models and lakehouse’s using medallion architecture that integrate data from Azure Data Lake Storage Gen2, Azure SQL databases, and other sources for consumption in Power BI and Synapse SQL pools.

Develop, maintain, and optimize semantic data models using Azure Synapse Analytics / Fabric, Spark notebooks.

Ensure data model accuracy, scalability, and performance.

Use PySpark within Azure notebooks to extract, transform, and load (ETL/ELT) data from raw formats (e.g. Delta, Parquet, CSV) stored in ADLS Gen2.

Implement data transformation pipelines and workflows in PySpark.

Partner with DevOps teams to deploy and maintain data models in production environments.

Optimizing data models from various sources such as Azure SQL databases and Azure Data Lake Storage Gen2 for usage in Power BI

Ensure efficient querying of large datasets and troubleshoot any performance issues related to data retrieval or processing.

Create and maintain detailed documentation and schematics of data models, metadata, and processes using suitable tools, such as Visio.

Develop and enforce data governance and data modelling standards to ensure consistency and accuracy across all models.

Ensure all data modelling practices adhere to security and compliance requirements, including role-based access control, encryption, and data privacy laws (e.g., GDPR).

Applying RL/CL/OL security within the data models as defined by business use and target audience