Data Engineer Hybrid London - Jefferson Frank
Description
AWS Data Engineer - Hybrid - £70,000
The Data Engineer forms an essential part of the Data Science Team, primarily by manipulating, cleansing and transforming data to be 'machine learning ready'.
Responsibilities:
- Leading on data engineering and ETL tasks which will exploit the AWS machine learning stack to examine novel machine learning problems within the rail industry.
- Working closely with data scientists by preparing data for them to develop statistical algorithms to implement solutions to key business challenges and advising on the best approach to do this.
- Develop best practices for ML Ops, engineering tasks, code development, code deployment, ethics, and approach to productionising solutions.
- Provide quality assurance of engineering tasks by code checking and any other practices necessary
- Conducting feasibility and practicality testing of business challengeled machine learning ideas, to help strengthen the data science portfolio.
- Mapping out data feeds and systems in collaboration with Solutions Architects and the IT Team, to then build richer pools of data for proof of concept/production solution design and build.
Job Requirements:
- A proven track record of creating and designing data pipelines, exposing and linking data from multiple systems
- Experience with security and monitoring best practice, preferably using AWS Cloud infrastructure
- A good understanding of coding best practices and experience with code and data versioning (using Git/CodeCommit), code quality and optimisation, error handling, logging, monitoring, validation and alerting.
- Experience of iteratively making data 'machine learning ready' preferably within AWS machine learning stack (primarily SageMaker)
- Fluent in writing well tested, readable code using Python that is capable of processing large volumes of data.
- An excellent command of the basic libraries for data science (e.g. NumPy, Pandas)
- Experience in writing complex queries to gather insight against relational and nonrelational data sources.
- Technical experience of mapping out data feeds to integrate and separate data to produce, transform and test new machine learning ideas
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