Data Engineer: SQL
Industry: Analytics/Data Consulting
Location: London OR Germany (Dusseldorf)
Founded: +8 years ago
Travel: Variable (10-40%)
Company Size: 40+ (majority in London)
We know that there are all kinds of data engineers, with varying levels of knowledge about different platforms and data sources. We’re more interested in candidates who are hard-working, smart and love what they do in the field of Analytics & Data. We care more about overall data and programming skills rather than experience with a certain database platform. Whether you have worked primarily in SQL Server, Postgres or something else, we only ask that you have a strong understanding of building data pipelines and can apply your skills within our fast paced and agile approach.
While some companies may hide their data engineers away in some dungeon, data engineers with us are social, friendly creatures. That is why being able to communicate with clients day in and day out is especially valuable. Our data engineers must be able to work closely with users in order to understand their needs and help them as best as possible.
Data Engineers can expect to work on diverse projects ranging from a few days to several months. Many of these projects include solving data acquisition, integration and management problems for some of the largest organizations in the world. Projects also include working with disparate data sources (Relational databases, flat files, Excel, HDFS/Big Data systems, high performance analytical databases, etc.) to unify client data, creating ETL processes based on client needs and managing client expectations.
· Excellent SQL skills (beyond just querying)
· Programming ability (Python, Java, C#, PHP, etc.)
· Strong ETL skills using GUI based tools or code based patterns
· Understanding of data modeling principles
· Excellent verbal and written communication skills
· Business acumen
· Strong problem solving skills
· Easily adaptable and flexible to changing situations
· Passion for delivering compelling solutions that exceed client expectations
· Experience with software engineering practices
· Experience with modern data engineering practices and frameworks
· Experience with integration from API sources