In recent times, the importance of big data is growing rapidly and is making the task of data engineers even more crucial with the passage of time. There are several facts and responsibilities of data engineers in the financial markets which we will discuss in this article.
This article covers:
- What is data engineering?
- Responsibilities in the field of data engineering
- Data engineering in the financial markets
- Data scientists vs data engineers
- Future of data engineering
What is Data Engineering?
Data engineering is a field in which data preparation meant for analysis in the enterprise takes place. Data preparation, here, implies the individual who constructs and tests the data. This process leads to such data which can be used productively for implementing in the analysis required by a particular enterprise. This data ready for the use builds the data architecture.
Data engineers are experienced to develop and manage large volumes of data. Also, one of the main responsibilities of data engineers is to aid data scientists to convert raw data into clean and usable data.
Raw data, here, implies the data which is extracted directly from the source and can consist of several issues such as duplicates, non-stationarity etc. Clean and usable data means the data which is ready for being used for various purposes in trading such as backtesting, analysis and forecasting the trades in the future.
Next, we will find out the responsibilities in the field of data engineering.
Responsibilities in the Field of Data Engineering
Data engineering is usually done for providing the enterprise with accurate data and requires proficiency in the programming languages such as Python, Java etc.
Simultaneously, data engineers have the following responsibilities:
- Support data scientist/analyst
- Manage data
- Divided into three types of data engineers
- Keep evolving
Support data scientist/analyst
Data engineers support the data scientist/ analyst in carrying out the operations based on the optimised data. Data engineers are mainly responsible for creating and maintaining the data infrastructure.
Data engineers are basically needed to manage the data also. Their responsibilities do not end at creating the optimised data for professional use. They also need to manage the data further which implies making sure there are no further errors, is easily accessible and reliable.
Divided into three types of data engineers
There are usually three types of data engineers namely:
There are some data engineers who do all the work of creating the data pipeline such as retrieving the data from the sources to processing it and doing the final analysis. This procedure takes up the entire skillset of a data scientist as well. This is required for small companies or the teams which do not have much of the staff for specialization.
They are required in the mid-sized companies which have complex data needs and need the data team to conduct a lot of work that requires the background of distributed systems and computer science.
These data engineers are usually found in the large companies with their data distributed across the databases. There are various data analysts in such companies and the data engineers are required to pull the information from the main application of the database into the analytics database.
Visit QuantInsti for additional insight on this topic: https://blog.quantinsti.com/data-engineering/
Disclosure: Interactive Brokers
Information posted on IBKR Traders’ Insight that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Traders’ Insight are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.
This material is from QuantInsti and is being posted with permission from QuantInsti. The views expressed in this material are solely those of the author and/or QuantInsti and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.