Data Engineer

One of the world’s leading sports betting companies, having experienced rapid success over the last decade and having grown their marketing spend exponentially, they are now a regular presence at high profile sporting events. They are now making significant hires across its data function to help drive commercial performance amid continued success.

The company operates a casual office environment in central London with an on-site barista, regular visits from leading sports stars and breakout rooms all over the complex. Fantastic development and progression opportunities mean that rapid promotions are common.

Sat in a sizeable team of data engineers, the work you produce will have real commercial impact as you make sure vast amounts of data are where they need to be to enable the fast paced, data driven environment of sports betting to continue. Whether that’s developing innovative cutting-edge new features to meet business requirements, or taking part in code reviews whilst working as part of a high calibre agile team.

Due to the growth within the team, there is also an opportunity to get involved with line managing junior members of the team within a short space of time after joining.

You will work on multi year projects to deliver cutting edge projects across various departments such as customer, financials and gameplay. Utilising your expertise in Python and working with an exciting cloud tech stack this is truly a great next step in your data engineering career.

The successful candidate will possess the following essential skills:

• Strong proficiency within Python or Py Spark
• Data Engineering experience, ideally with an Azure background
• Significant experience with SQL (preferably SQL Server)
• A skilled communicator able to interact with stakeholders of varying seniority

It would be desirable if you were to possess:

• Experience with cloud based tools, ideally Azure
• Experience with C#

PLEASE NOTE:
Regrettably, this client is unfortunately unable to offer Visa sponsorship

Job Owner: d.prosser

Tagged as:
Azure, Python, Spark, SQL