Data is one of the most important assets within any financial institution. The business of banking and financial management has multi-million transactions at any one time, which all contribute to the immense ocean of data. It is critical for all FinTech, Trading, Banking and Hedge Fund firms, etc. to utilise this data to give them a competitive advantage within the markets and therein make profitable business decisions.

We have seen the rising demand for Data Scientists across FinTech, especially the last couple of years, and this will continue to grow as they are providing significant impact to all businesses across finance, but an equally important data maestro profession went slightly under the radar. This being the Data Engineer.

In the last six to nine months, I have seen a rapid increase in Data Engineers being sought by leading financial technology firms. This is predominantly down to the adoption of AWS and Azure within these firms and the pure benefit they bring to data pipeline streaming processes. By owning the data platform and providing engineered systems, they allow quant teams to make valuable trading strategies as a resultant. They are also critical with smaller FinTech firms where they don’t have the luxury of an official data infrastructure in play for them to build the whole data eco-system.

What can you expect from data engineers? Here are some skills to look out for:

– Solid coding skills with strong analytical and data visualisation skills

– Strong performance tuning and data optimisation skills

– Data integration skills in abundance

– Data modelling & ETL skills

– Deep knowledge of cloud computing services

– Skilled with both relational and non-relational database systems

– An ability to work with masses of structured and unstructured data

We’re predicting a continued rise in demand for Data Engineers as they become an increasingly vital cog in the machine for the build and maintenance of the data platform, which can hold the key to competitive market information or data sets for efficient predictive modelling.

Also, it is important to understand that not all data is clean and efficient, therefore data engineers will bring a craving to improve data quality, efficiency and reliability.

To discuss this further or for information about the latest Data Engineering roles, please give me a call or email directly on jon.kay@harringtonstarr.com.