Cloud data
We ask candidates to choose batch or streaming components, control BigQuery cost and explain ordering, duplication and replay behaviour in an event-driven pipeline.
- BigQuery
- Dataflow
- Pub/Sub
- GCP
- Google Cloud
UK, Europe, USA
Data engineer recruitment
Data engineers screened for pipeline reliability, storage decisions and production-scale processing.
Instruct us
Technical screening by developers and CTOs is included.
Data engineering recruitment for real pipelines
We recruit data engineers for ingestion, transformation, storage and event-driven systems. Technical screening covers pipeline design, SQL and NoSQL trade-offs, cloud data services, observability and the operational behaviour of distributed data workflows.
When to hire Data engineers: A company building its first dedicated data platform. A high-volume ingestion or event-processing system. A product team improving data reliability and performance.
Technologies, requirements and soft skills
We map the data sources, volume, latency, consumers and cloud environment before deciding what experience is essential.
Storage
We test data modelling, partitioning, indexing and consistency decisions by asking how access patterns and retention requirements change the storage design.
- PostgreSQL
- MySQL
- SQL Server
- MongoDB
- Redis
- NoSQL
Pipelines
We ask how pipelines recover from partial failure, remain idempotent and expose data quality problems before incorrect records reach downstream products.
- Big Data
- PowerBI
- Looker
- Temporal
- IoT
- Microservices
Soft skills and working style
We look for people who question surprising data, investigate causes rather than patch symptoms and can explain quality, uncertainty and trade-offs to colleagues who do not work directly with data systems.
- Analytical precision
- Healthy scepticism
- Data empathy
- Root-cause thinking
- Stakeholder translation
How we assess data engineers
Candidates work through the movement, quality and failure behaviour of data rather than describing tools in isolation.
Pipeline design
Batch and streaming choices, orchestration, idempotency and recovery.
Data modelling
Schemas, query patterns, warehouse design and storage trade-offs.
Operational quality
Testing, observability, data quality, performance and incident diagnosis.
What every candidate needs to demonstrate
Tool names are not enough. Across every role, we look for technical depth, disciplined use of AI and an honest approach to solving unfamiliar problems.
Understands what happens behind the scenes
We keep asking why and how until we reach the underlying behaviour. Candidates should understand what their framework, runtime, database, browser, cloud service or design tool is doing for them, how they work behind the scenes (and why), and how they would investigate a failure without relying on copying fixes from ChatGPT.
Uses AI with engineering discipline
We expect candidates to use AI productively, but never as a substitute for judgement. They need to explain how they verify generated code, designs and tests, protect confidential data, catch unsafe assumptions and remain accountable for the result. We expect them to use MCPs, skills, and goals. So basically - we are looking for structured AI-assisted work, not vibe coding, vibe designing or vibe testing.
Combines initiative with honesty
Strong candidates have a can-do mindset and will investigate, experiment and learn when the answer is not obvious. At the same time, they are fair and transparent about what they know, what they have not done before and when they need support.
Looking to hire?
Give us a few details about the role and your hiring plans. We'll assess your needs and get back to you with a practical next step.
Frequently Asked Questions
We recruit for relational and NoSQL data platforms, batch and streaming pipelines, cloud warehouses, event-driven systems and advanced ingestion workloads. Relevant technologies have included BigQuery, Dataflow, Pub/Sub, SQL and Temporal.
Yes. The assessment can cover pipeline architecture, data modelling, query behaviour, performance and operational reliability.
Yes. We define the cloud platform, storage, processing, orchestration, networking, access controls and observability the role owns, then tailor sourcing and technical assessment to that environment.