A Data Engineer sits between the technology and commercial teams and is responsible for extracting, transforming, loading and often presenting data to internal and external stakeholders in an effective and efficient way.
Whilst these are very technically challenging roles, often requiring deep SQL and Python knowledge and strong numeracy skills, successful data engineers also need to be excellent communicators, acting as the bridge between different departments in the organisation.
Depending on the type of role and/or the company size, data engineers will be tasked with developing accurate and optimal data retrieval methods and then presenting that surfaced data in dashboards or reports, in a visually appealing and understandable way.
In larger, enterprise-level companies, a Data Engineer would often work with Data Scientists to analyse, interpret and present data, whilst in smaller firms it’s often the Data Engineer themselves who has to analyse, interpret and present the data.
Types of Data Engineering Roles:
According to DataQuest.io, one of the world’s leading providers of Data Science courses, a Data Engineer fits into three main categories:
Generalist Data Engineers tend to work for smaller companies and will act as one of the few (if not only) data-focused employees. They surface, analyse and present data to key stakeholders, having an incredible influence on product/service direction or market opportunities.
Pipeline-focused Data Engineers use their technical skills to surface data and automate data flows into either a cloud solution or a Data Warehouse. Pipeline-focused Data Engineers often work alongside Data Scientists to transform data into an analysis-ready format and can often be found in mid-sized companies.
Database-centric Data Engineers are often found in large companies where each step of the data engineering process is managed by different teams or personnel across distributed systems. Database-focused Data Engineers are tasked with setting-up and populating analytics databases, fine tuning them for better performance and creating table schemas.
The Data Engineering Skill-Set
Whilst excellent Python and/or SQL knowledge is commonly required for Data Engineering positions there are a few other highly desired skills that would see your referral command a higher base-salary. These include Scala, Apache Spark and Apache Hadoop, Data Warehousing, Linux, AWS and Java.
It's highly recommended that candidates have an academic background in statistics, analytics, computer science or mathematics ideally to at least undergraduate level and in some cases, up to PhD.
Our Data referral opportunities
We currently have multiple Data Engineering referral opportunities on the Referment platform with clients in the Hedge Fund, FinTech, Market Making and Asset Management spaces, with salaries ranging from £45,000 up to £140,000, depending on the role!
Take a look at our Data roles below. Refer someone you know to their next dream data role and we'll reward you with between £250 and £500 when they complete a first-round interview. Who will you refer?
FinTech Data jobs in London:
Data Engineer - 3+yrs exp. | Up to £80,000
Data Scientist - 2+yrs exp. | Up to £70,000
Data Quality Engineer - 3+yrs exp. | Up to £70,000
Hedge Fund Data jobs in London:
Big Data Engineer - 3+yrs exp. | Up to £140,000
Python Data Engineer - 2+yrs exp. | Up to £90,000
Market Making Data jobs in London:
DataOps Engineer - 2+yrs exp. | Up to £80,000
Asset Management Data jobs in London:
Data Engineer (Commodities) - 4+yrs exp. | Up to £130,000