Data engineer recruitmentClients in the UK, Europe and USA
Data engineering CVs are lists of tools. A data engineer reads every one before you do and tests what matters: duplicates, late events, partial failure and data quality.
01
A recruiter can match BigQuery or Dataflow to a job spec. They can’t tell whether the candidate has kept a pipeline idempotent or caught bad data before it reached a report. So you re-read every CV, and weak candidates still reach your interview.
02
You need CVs that pass engineering’s interview, so you can send a shortlist it trusts. An engineer reads every CV before it reaches you. Fewer CVs, but better ones.
58%
of HR and recruitment leads who use agencies say the main reason engineering rejects agency candidates is that the CV looked good, but the person couldn’t do the job.
A developer or CTO who works in the stack interviews every candidate. Each of these is a question you would otherwise only answer in your own interview.
Whether they can choose batch or streaming components, control cost and explain ordering, duplication and replay behaviour.
How they handle data modelling, partitioning, indexing and consistency, and how access patterns and retention change the design.
How their pipelines recover from partial failure, stay idempotent and expose data quality problems before incorrect records reach downstream products.
They can explain what the warehouse, queue or orchestrator does underneath, and investigate a surprising number instead of patching the symptom.
They can explain how they check generated SQL and pipeline code, and keep personal and confidential data out of AI tools.
They tell colleagues what the data can and cannot show, and say plainly what they have not worked with before.
We recruit data engineers for ingestion, transformation, storage and event-driven systems. A data engineer screens every candidate on pipeline design, SQL and NoSQL trade-offs, cloud data services, observability and how distributed data workflows behave in production.
Either way you get about 3 to 5 screened CVs a week, per role.
We do
You do
We do
You do
“Metamindz helped us recruit our now CTO, reviewing CVs as they came in and then putting the CTO through a thorough 90-minute live 360 technical test. Feedback from our now CTO of the test was glowing and comms from Metamindz throughout the entire process were excellent. Highly recommend!”
Rupert Boddington
Co-Founder, Pockla
“Worked with Lev and Max. We worked with MetaMindz to help us interview and assess software engineering candidates. They were professional and great from the outset and throughout, great speed too. Would 100% recommend and we will be working with them again.”
Alexander Bushe
CEO, Fuller Focus
“We found it really easy working with Lev and the team to hire for our lead developer role and despite it still being early days, our new hire is making an impact already!” source
Billy Whiffen
Google review
£5,000
Fixed fee for your first role
15% to 17%
Commission on each further hire
Deep technical interview (Route 2)£300 an hour
A 1.5 to 2 hour live-coding and technical grilling session per final candidate.
All fees exclude VAT.
Tell us about the roleWe 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.
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.
Listed for reference. We use these to scope the brief with you. A keyword on a CV is where screening starts, and the interview decides whether the candidate can do the work.
Teams usually bring us in for: A company building its first dedicated data platform. A high-volume ingestion or event-processing system. A product team improving data reliability and performance.