Job Description
Data Engineer
Remote Role/Hybrid: Travel to Cardiff Once a month if outside of area
£45,000
MBN is delighted to partner with a leading utilities company in search for a Data Engineer.
The Data Engineering team play a huge role in providing quality and reliable data to feed into analytics products used by the business for decision making.
There will be opportunities to get involved with the latest cloud-based technologies and to be part of a collaborative, supportive, and high-performing team within the organisation.
You will be undertaking both database administration activities, and building pipelines to feed data into analytics outputs, such as predictive models, etc. You will also support the progression of their cloud-based data platform by contributing to development initiatives using tools within the Azure (cloud) data stack.
Responsibilities:
Design and implement high quality data engineering solutions on both on-premises and cloud-based data stacks, ensuring compliance with architectural and data security requirements
Being able to work collaboratively as part of a team, whilst also being trusted to work individually where necessary. Approaching collaborative projects with others in a positive manner to overcome issues and challenges
Working closely with technical colleagues within key teams within Integrated Technology Services (ITS) to ensure relevant information technology components and services are in place to accommodate developed solutions
Requirements:
Relevant undergraduate degree in computer science, mathematics or related discipline, relevant long-term experience; or MCSA/MTA certification.
Experience in any of the following or related fields: database management, data architecture, data platform management
Experience using cloud-based data stack (e.g. Azure, AWS, GCP)
An awareness of typical ingestion patterns (e.g. ETL, ELT)
Knowledge of typical data modelling approaches (e.g. Kimball)
Python and SQL programming capabilities
Experience using the Microsoft data stack (especially Azure Data Lake, Azure Synapse, Azure Data Factory)
Data security approaches – e.g. permission models (AD, role-based, etc.) and techniques to protect data in certain circumstances (e.g. encryption, masking)
An awareness of the full software/reporting lifecycle from planning and design through deployment and maintenance. From requirement gathering to reporting or dashboard solution.
Awareness of the Microsoft BI Stack, including Analysis Services, Integration Services and Reporting Services, and Power BI