Cloud & DevOps Staffing — Engineers Who’ve Architected at Scale
Cloud transformation can't wait for a 90-day hiring process. Your infrastructure needs are urgent—whether you're migrating legacy systems, optimizing cloud costs, or building resilient DevOps pipelines. Finding cloud architects with hands-on AWS, Azure, or GCP experience who also understand your specific infrastructure challenges requires precision. You also need Data Engineering Experts who understand data analysis and AI/ML in Databricks and Snowflake.
Cloud engineers, DevOps specialists, and data engineers share one trait that makes bad placements expensive: they work in production. There is no room for someone learning on the job.
Why Cloud and DevOps Hires Go Wrong
The candidate had AWS on their resume. What they had was a personal account with a few EC2 instances. Your environment runs multi-account VPCs, automated deployments across three regions, and an on-call rotation that pages at 3 a.m.
Cloud, DevOps, and data engineering roles fail for the same reason infrastructure roles fail: the work is environment-specific. A DevOps engineer who’s managed Kubernetes at 10,000 pods is a categorically different hire from one who’s run a handful of services in a staging cluster. A data engineer who’s built real-time pipelines for a fintech company is not the same as one who’s done batch ETL for a small analytics team. These differences don’t show up in a resume screen. They show up in production.
The cost of a wrong hire in these disciplines is high. Misconfigured infrastructure creates security exposure. Fragile pipelines break data-dependent product features. A DevOps engineer who can’t keep up with your deployment cadence becomes a bottleneck for every team downstream.
A recruiter who understands the domain closes these gaps before the offer letter goes out.
Three Disciplines. One Vetting Standard.
Cloud engineering, DevOps, and data engineering each require domain-specific evaluation. We treat them as distinct disciplines, not interchangeable categories under the same umbrella.
Cloud Engineering
We source against your specific cloud provider and service footprint. An AWS engineer with deep Lambda and ECS experience is a different hire from an Azure engineer who’s built enterprise Active Directory integrations, or a GCP engineer running BigQuery at scale. We match candidates to the services and architecture patterns you’re actually running, not cloud engineering in general.
DevOps and Platform Engineering
We evaluate DevOps candidates against your actual deployment cadence, CI/CD toolchain, and infrastructure-as-code approach. Platform engineers who’ve owned developer experience at scale, SREs who’ve written runbooks under real incident pressure, and release engineers who’ve shipped daily across distributed teams — these are different profiles. We know the difference before we source.
Data Engineering
Data engineering has fragmented into a wide set of specializations: streaming vs. batch, warehouse-native vs. orchestration-heavy, analytics-focused vs. ML-adjacent. We identify where your data stack sits and match candidates who’ve operated in that specific context — not engineers who’ve touched data work but never owned a production pipeline that other teams depend on.
The result: candidates evaluated against your actual environment, your stack, and your scale — across all three disciplines.
Roles We Place
Cloud Engineering
DevOps and Platform Engineering
Data Engineering
If the role involves building or operating the systems that ship code or move data at scale, we can source it.
What the Process Looks Like for You
Intake Call
We spend 30 minutes mapping your stack: cloud provider and services, CI/CD toolchain, data infrastructure, team structure, deployment cadence, and what the last hire got wrong. These details change the search entirely, and we pull them out before we source anything.
Candidate Sourcing and Vetting
We work our network and evaluate candidates against your environment profile, production experience requirements, and team fit. You don’t see anyone who hasn’t cleared all three. That means your engineers’ time goes toward real interviews, not filtering out candidates who list Kubernetes but have never run it in production.
Shortlist Delivery
You receive a curated shortlist of qualified candidates. Each profile includes our assessment of fit — stack alignment, production experience depth, team and culture match — not just a forwarded resume.
Your Interviews
You run the process from here. We stay available to support scheduling, candidate questions, and recalibration if the first shortlist needs adjusting based on what your technical interviews reveal.
Placement and Follow-Through
After an offer is accepted, we stay engaged. If something isn’t working, we work with you to understand what happened and address it. A placement that doesn’t hold isn’t a win for anyone — and it’s not something we consider a finished job.
Related Services
Building a complete technical team? We staff across the full IT stack.
Frequently Asked Questions
A generalist recruiter searches for “DevOps” and sends you whoever has it on their resume. A DevOps recruiter evaluates the specific CI/CD toolchain you run, the deployment cadence you operate at, and whether a candidate has actually owned infrastructure in production or just configured pipelines in a lab. At Teak Talent, we assess hands-on experience with your specific stack — AWS, Azure, or GCP; Terraform or Pulumi; Kubernetes at your scale — before anything reaches your shortlist.
For most roles, expect a shortlist within a few business days of the intake call. Senior cloud architects, staff-level platform engineers, and specialized data engineers may take longer. We give you an honest timeline upfront, not an optimistic one that slips.
Yes. We work across all three engagement types. Cloud migration projects and pipeline buildouts often call for contract staffing. Long-term platform ownership typically warrants permanent placement. The intake call determines which structure fits your timeline and budget.
Yes. We source against your specific cloud environment, not cloud engineering in general. An AWS engineer with deep Lambda and ECS experience is a different hire from an Azure engineer who’s built enterprise AD integrations. We match candidates to the provider and services you’re actually running, not the provider everyone’s heard of.
We place engineers across cloud architecture, DevOps and platform engineering, site reliability engineering, data engineering, data architecture, MLOps, and related technical leadership roles. We staff for companies across industries including SaaS, fintech, healthcare technology, and enterprise. If it involves building, shipping, or scaling infrastructure or data systems in production, we can source it.
Production Can’t Wait for a Resume Dump
Teak Talent places cloud engineers, DevOps specialists, and data engineers vetted for your stack, your scale, and your deployment standards. If your current search is stalling, let’s talk.
Get Your ShortlistReady to Scale Your Infrastructure Team?
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