You know you have a data problem. You’re looking for a way to fix it. Bringing data professionals into the business and building a data function will enable you to access your data and utilise it in a more effective and efficient way. If you’ve decided to solve your data problem by building a data solution internally, you may need to hire a Data Engineer.
Data Engineers are responsible for building and maintaining the data infrastructure. At full scale, a complete data team will include Data Analysts, Engineers and Scientists. Before starting your hiring process, first take a look at our defining data professionals blog to understand the difference between each of these roles.
Top 6 skills of a Data Engineer
- Interact with Analysts – Data engineers enable analysts by providing access to data to analyse. A good Data Engineer will be able to communicate with Analysts in order to provide them with the right data at the right time.
- SQL and Python – All Data Engineers need SQL and Python. SQL is a query language which is mainly used for accessing and extracting data from a database. Whilst Python is a programming language that enables automation and processing data, which Engineers need to have experience with.
- The ability to deploy systems which can run autonomously (simple DevOps) – Modern data engineering is mostly done in cloud environments so familiarity with GitHub and CI/CD programming is a must. Version control tracks changes and ensures that the team has access to the most up to date version
- Experience with modern engineering frameworks for data engineering – An understanding of Airflow and Apache Spark is definitely a plus. You don’t have to implement frameworks, but you are likely to find yourself reinventing the wheel if you go it wholly alone.
- Ability to model data well – Taking messy data from source systems and transforming it into a clean usable form is a hugely valuable aspect of data engineering.
- Experience optimising data pipelines – A Data Engineer won’t only build data pipelines, but will be required to improve them too.
How to hire a Data Engineer
- Test programming languages – Data Engineers should have expertise working in both SQL and Python. SQL allows engineers to establish, query, and manage database systems. Python is necessary for creating data pipelines, writing ELT scripts and data analysis.
- Extract data from APIs – To test this, provide candidates with an open API, then ask them to extract data from it and explain why they chose that data. You are looking to see how they accessed, represent and store data and the speed at which they achieved this. In this test you are looking at three key areas:
Speed – How quickly did the candidate carry out the work?
Reliability – Is the data consumable? That is to say, is the data usable, not in a nested JSON for instance.
Security – Should this data be encrypted? Has it been? Why has the candidate chosen to store it there?
- Collaboration and communication skills – It’s important that a Data Engineer has soft skills such as communication, teamwork and time management. Strong communication skills – the ability to explain technical concepts to non-technical business leaders – are key. Asking experience based questions will uncover the applicant’s attitude to failure and learning from it, collaborating with teams and their enthusiasm for the role.
The alternative solution to hiring engineers
kleene’s SaaS software makes this easy, so you won’t need to make expensive data engineering hires. We provide scale-ups with the data infrastructure necessary to build their single source of truth and empower Analysts to deliver value (without the need for data engineering!). Our end-to-end automation and analysis removes the headache of data. Don’t worry if there isn’t in-house data expertise, kleene can handle your strategy, report building and much more!
Book a demo today to start solving your data problem!