Posted in: Engineering in London | Posted: |
Data Engineer/Scientist
London (Hybrid)
£50,000-£60,000 (+bens)
83DATA are partered with a leading Internet provider seeking the skills of a Data Engineer/Scientist. The successful candidate can expect to be tasked to connect data sources to pre-existing Azure data infrastructure. You will lead projects with a data centric approach, for this reason, a passion and aptitude for critical analysis of large and complex datasets is vital. The role will directly report to the Head of Data - a high degree of resilience is required to provide solutions to the executive board thus enabling data driven decisions.
Responsibilites:
Build data engineering pipelines to ingest, transform, store, and serve high quality data for Data Scientists
Writing and tuning complex data pipelines, workflows, and data structures.
Identify opportunities for increasing performance, minimising costs and increase platform monitoring.
Background of using data analysis tools and software (e.g., SQL, SAS, Python and R).
Experience with components such as Azure Active Directory, Management Groups, Subscriptions, vNets, Route Tables, NSG's and Azure Firewalls.
Ensuring data security and maintaining quality assurance across all systems.
Promote a data driven culture, advocating for the use of data and analytics in decision making and process optimisation.
Using Predictive modeling solutions for current data and generating a model to help predict future outcomes.Requirements:
A Computer Science/Programming Degree.
SQL - Advanced Proficiency
Python - Advanced Proficiency
Experienced in Machine Learning and predictive modelling.
Background working with consultancy services.
Highly experienced with MS Azure cloud platforms. 3 years minimum.
Understanding of building resilient multi-site architectures.
Proven ability to develop and execute user retention strategies via CRM and product.
Experience of large complex data migrations
Proven experience in cost optimisation and performance efficiency
Proven experience in Data Science and/or Machine Learning including an understanding of fundamental technologies and processes involved in the research and development of data science models
