Senior Data Engineer

Main purpose of job:

Responsible for facilitating the implementation of the data strategy, under the strategic direction of the Data Steering Group, through iterative technical delivery of a modern data platform solution that provides the business with the following:

• Improved data quality and consistency,
• More readily and reliably accessible MI,
• Enhanced data-driven business insights,
• Leveraging of digitalisation and automated solutions to achieve greater resource and process efficiencies.

This will include contributing towards the design and building of a modern Data platform solution to handle large and complex data sets.

Key tasks:

1. Design Data Lake and Data Warehouse solutions
2. Design Data Models using Data Vault and Dimensional modelling methods
3. Implement automated, reusable and efficient batch data pipelines and streaming data pipelines
4. Work closely with Governance and Quality teams to ensure metadata, catalogue, lineage and known solutions to data issues are optimised
5. Work closely with other data specialists and technicians in the organisation, such as Actuarial and Finance, to help with their data presentation requirements and help deliver data for visualisation solutions, such as Power BI
6. Build solutions to handle Personal and Sensitive data

Key personal skills required:

1. Has the ability to build collaborative relationships with stakeholders from across the organisation, working together to deliver business-driven solutions
2. Strong accuracy and attention to detail. Skilled at problem solving
3. Self-motivated and forward-thinking, looks for ways to innovate and add value
4. Good communication skills, both written and verbal
5. Is an advocate for change in the organisation, recognising that solutions should be tailored and delivered to ensure alignment to business priorities and strategy
6. Ability to balance multiple priorities, by focusing on timely delivery of quality outputs

Key technical skills, knowledge and professional qualifications provided:

1. Databases – has a deep understanding of relational databases
2. Use DevOps methods and work in an Agile environment
3. Programming - expertise in Python, Java, Scala, or other programming languages used to build data pipelines, implement data transformations, and automate data workflows
4. Has expertise in SQL
5. AI and Machine Learning knowledge as it applies to end-to-end data production
6. Has a strong knowledge of data visualisation tools knowledge such as Power BI, Tableau, QlikView etc
7. Familiarity with technical data structures, data pipelines and data science techniques, understanding complex systems
8. ETL Tools – has strong knowledge of ETL tools essential for building data pipelines effectively
9. Data Architecture – knowledge of data architecture, and must work with data architect to optimise solutions
10. Cloud Computing - proficiency in cloud platforms like AWS, Azure, or Google Cloud is necessary to deploy and manage data solutions in the cloud