Playbook Example | Data Engineers

Below is an example of a recruitment playbook for the role of data engineer. If you are a series A scale-up, reach out to QuantumLight to get access to the full playbook around the role you are looking for.

About the role

All engineers, regardless of specialisation, are expected to meet a high bar for software fundamentals, covering system design, code quality, and understanding of the full development lifecycle. Specialisation matters, but every engineer is expected to think holistically and write production-grade code. Specialist roles such as data engineering build on this foundation but do not replace it.

Skills Scorecards & Talent Bar

The hiring bar for engineers is anchored in a consistent set of core skills:

• Software Lifecycle: the ability to manage the various stages of a software project from conception to retirement

• Architecture: the ability to design and plan the structure, organization, and components of a software system or application

• Programming (General): the ability to write, understand, and create computer programs or software using programming languages

• Problem-solving: ability to logically break down complex problems from first principles and solve them with common sense and critical thinking

On top of that, Data Engineer specialised skills include:

• Programming (Python): the ability to write, understand, and work with the Python programming language

• Data Engineering: the ability to design, construct, and maintain data pipelines and infrastructure that enable the efficient and reliable collection, storage, and processing of data

• Infrastructure (Iac Cloud, etc.): the ability to set up, configure, manage, and optimise cloud-based computing environments and resources

Depending on the seniority you’re hiring for, expectations will vary:

These skills are assessed throughout the interview process using structured scorecards with observable behaviours, and candidates are expected to meet or exceed the minimum threshold across all relevant areas. For example, for the Python Programming skill:

Sourcing guidelines

Identifying strong candidates for this role starts with quickly filtering through profiles. The following example describes the screening guidelines for surfacing high-potential candidates for the role of Data Engineer:

Hiring process

The hiring process is designed to gather high-quality, structured signals across the core skill areas while moving quickly enough to keep top candidates engaged. Each stage is purpose-built to test a specific set of capabilities, with no duplication between interviews. Scorecards are used consistently to enable clear, independent evaluation and fast debriefs.

The below reflects a typical mid-level process for a Data Engineer: