Data Engineering • Qualifications in Computer Science, IT or a related discipline • Data engineer experience • Demonstrated experience in developing, configuring, validating, and managing Microsoft cloud platforms, Data Factory, SQL Server, Databricks. • Understanding of Machine Learning concepts. • Demonstrated experience in DevOps and/or DataOps. • Documented, hands-on ETL development using tools like SQL Server Integration Services or Azure Data Factory.

2.

Data modelling and database management • Data modelling experience • Strong knowledge of database structure systems, data analytics, data mining and data modelling techniques. • Documented experience in data analysis and management, with excellent analytical problem-solving abilities. • Demonstrated ability to combine multiple large and complex datasets to deliver high-quality datasets.

3.

Coding and systems administration • Demonstrated experience in coding languages (such as SQL, Python, R, C Sharp/C++, Powershell) • Systems administration experience • Database / Data Lake management experience from design, development, delivery, validation and ongoing maintenance.

4.

Reporting and visualisation • Experience using and developing engineering solutions for data visualisation tools such as RStudio, Power BI, SAS, Qlik, Tableau. • An applied understanding of UI design concepts.

5.

Communication and Collaboration • Well-developed team collaboration and communication skills • Demonstrated ability to work under limited direction and accountability for completion of work. • Demonstrated ability to provide advice and develop solutions within multi-disciplinary work environments. • Demonstrated ability to communicate complex technical concepts to non-technical staff.

Desirable criteria

1.

• Experience with the deployment and maintenance of machine learning models. • Experience developing datasets API delivery for a variety of scenarios • Experience with machine learning technologies. • Apache Spark (Scala or Python) experience. • Spark SQL coding experience. • Automation experience. • End-to-end Machine Learning pipeline development. • Proven experience working on Data Science projects.

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