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:
- Uses pythonic language constructs (ex.: for statement, decorators)
- Uses the basic features of the selected frameworks (doesn't reinvent the wheel), examples: uses FastAPI but only as a web router; uses SQLAlchemy but on very basic queries and updates
- Uses type hints appropriately and verifies it using static type checking tools
- Can recognise non-standard Python code based on Company coding guidelines and refine it
- Ability to use the selected libraries and frameworks to their full potential and with best practices
- Able to isolate and debug code issues
- Able to write and publish custom libraries
- Solves problems elegantly by using the full potential of the language
- Is conscious of optimisation techniques on code and deployment models
- Can write Python extensions
- Deep knowledge of how Python gets executed, either in threads, on event loops, etc.
- Can optimise performance on critical methods and services when required
- Can quickly pick up the pros and cons of new frameworks and libraries assessing its usage safety and effectiveness
- Understands Python byte code and can explain the language limitations and benefits with accuracy
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:
- Education in computer science or related field
- Experience working with software products using Python
- Experience with SQL & NoSQL databases
- Experience with Docker & Kubernetes
- Experience with public cloud systems (GCP or AWS)
- Experience with CI/CD, TDD and/or trunk-based development
- Experience working with Big Data/MPP analytics platform
- Ability to write easily understandable and maintainable code in multiple programming languages
- Awareness of data quality aspects in data warehousing systems
- ETL design (Airflow)
- Data warehousing and large scale data processing (Exasol, Trino, Iceberg)
- Infrastructure as code (Kubernetes, Helm, Terraform)
- Experience with SQL performance tuning
- Experience with anomaly/outlier detection
- Experience with notebook-based Data Science workflow
- Experience with monitoring and logging tools: NewRelic, Grafana, Prometheus, ELK
- Experience implementing Data Mesh principles
- Experience working in tech start-ups
- Low GPA
- Slow career progression
- Frequent change of jobs
- Doesn’t have end-to-end development experience
- Experience with only one programming language
- Working in a slow paced environment
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:
Purpose: filter out candidates that do not have the required experience
Conducted by: Recruiter
About: the Recruiter sells the role and screens the candidate’s experience, normally testing for minimum knowledge of Python and Data Engineering via short technical questions. What to look for:
- Minimum required Python knowledge
- Minimum required Data Engineering knowledge
- Minimum required level of English and communication skills
- Career progression, working their way from Junior to Mid to Senior to Lead
Purpose: assesses the Problem Solving skill
Conducted by: Certified Interviewer
About: designed to assess a candidate’s ability to approach unfamiliar, ambiguous problems using structured, first-principles thinking; based on a real case study, not technical in nature. What to look for:
- Clear, top-down thinking and logical structure
- Data-driven reasoning
- Evidence of pattern recognition or transferable mental models
- Strong questioning to clarify scope and constraints
Purpose: verify the candidate’s proficiency with the core Data Engineering toolkit and sound software lifecycle practices (Software Lifecycle, Data Engineering and Infrastructure)
Conducted by: Data Engineer
About: theoretical questions about Data Ops and Software Lifecycle, as well as examples of how the candidate approached these issues in real life. What to look for:
- Sound approach to data quality and productionism (usage of tests, CI, PRs etc)
- In-depth knowledge in the field of database transactions
- Proficient with practice and theory of databases
Purpose: establish if the candidate has the motivation and attitude to be an A-player and fits the core values of the company
Conducted by: Certified Interviewer
About: arguably the most important interview at the company, and the tool employed to “raise the bar” and hire high achievers with a culture of excellence, in line with the company’s values. What to look for:
- Track record of achievement
- Cultural fit with company values