City of London, London
£50000 - £70000 per annum + bonus
2 months ago
Data Engineer (NoSQL, Warehousing, ETL)
We are looking for an experienced Data Engineer (NoSQL, Warehousing, ETL) to join a leading global organisation, that are currently making waves and disrupting the financial world through cutting-edge research. We are looking for individuals experienced in dealing with high quantities of data and who can work on data infrastructure using big data and public cloud technologies. You must have at least 2 years of commercial experience.
We are global leaders in the Financial Industry, where we use Petabytes of data taken from the industry, and use it to help fuel the financial markets that allow investors to make clear and accurate decisions, financial institutions to reduce risk and even fight financial crime.
As a Data Engineer (NoSQL, Warehousing, ETL) within our business, you will be a part of a very dynamic and collaborative team, working with like-minded individuals across the world, in order to provide world-class solutions to our customers. You will be working with some of the most advanced technologies and can utilise your passion for solving problems to the full.
Realistically, what we're after is an experienced Data Engineer keen on solving problems and dealing with high quantities of data from the financial world. You should be experienced in typical data engineering techniques, including building infrastructure, working with data models and wranglers and monitoring data pipelines.
What we can offer as a Data Engineer (NoSQL, Warehousing, ETL):
- A chance to help improve the way an industry operates.
- The chance to work with a number of like-minded individuals keen on solving difficult problems.
- To work with some of the most advanced technologies.
- The opportunity to work at a company that gathers and processes petabytes of financial information and data.
Key Skills: Data Engineer, AWS, GCP, Azure, Python, Cloud, AWS, GCP, Comp Sci, BSc, MSc, PhD, Data Analysis, Financial Industry, Data Warehousing, Data Infrastructure, Data Pipelines, SQL, NoSQL, Big Data, Data Management, Message Queuing, Stream Processing, Metadata