Data Engineer

  • Full Time
  • London
  • Salary Guide: £30,000 - £65,000


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Exabel Limited

Exabel was founded in Oslo in 2016 based on the belief that the financial system will best serve society with fair, open and efficient markets. Exabel... Role: Data Engineer, Category: IT & Telecoms, Size: N/A

Data Engineer: Exabel Limited

Exabel was founded in Oslo in 2016 based on the belief that the financial system will best serve society with fair, open and efficient markets. Exabel delivers an analytics platform for any investment professional who wants to benefit from alternative data and modern data science tools in their investment process. It fulfils a growing need in financial marketswhile use of data including fundamental, market, proprietary and alternative data is critical for investment managers, modeling such data in house has become an excessive use of time and resources for all but the very largest investment firms.

Position: Data Engineer
Location: London
Hours: Full Time
Salary Guide: £30,000 – £65,000
Last Updated: 15 June 2022
Job Category: IT & Telecoms

Data Engineer Role:

Analyze and organize raw data . Build data systems and pipelines. Evaluate business needs and objectives. Interpret trends and patterns. Conduct complex data analysis and report on results . Prepare data for prescriptive and predictive modeling. Build algorithms and prototypes. Combine raw information from different sources. Explore ways to enhance data quality and reliability. Identify opportunities for data acquisition. Develop analytical tools and programs. Collaborate with data scientists and architects on several projects.

Other Duties:

  • Analyze and organize raw data 
  • Build data systems and pipelines
  • Evaluate business needs and objectives
  • Interpret trends and patterns
  • Conduct complex data analysis and report on results 
  • Prepare data for prescriptive and predictive modeling
  • Build algorithms and prototypes
  • Combine raw information from different sources
  • Explore ways to enhance data quality and reliability
  • Identify opportunities for data acquisition
  • Develop analytical tools and programs
  • Collaborate with data scientists and architects on several projects



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