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You've probably watched a model project slow to a crawl and assumed the model was the issue. More often, the problem is upstream: data scattered across systems, pipelines nobody owns, and quality checks that don't exist yet. Fixing that means hiring someone whose entire job is data infrastructure.
The median technical role takes 62 days to fill, and that's 62 days your team spends patching pipeline issues instead of building. KDCI can place a pre-vetted data engineer on your team in 7-14 days, at a flat monthly rate below a local hire. This page covers what the role does, how it differs from data scientists and ML engineers, and the channels available to hire one.
A data engineer builds and owns the pipelines that move data from wherever it originates to wherever it needs to be usable. In practice, that means they're the person who decides how raw data gets extracted from source systems, how it gets cleaned and structured in transit, and where it lands in a form other teams can actually work with. That work spans:
A data engineer's job ends at making data reliable and accessible. Analysis, predictions, and model-building all belong to different roles. Turning clean data into predictions is what you'd hire Data Scientists for, and training models on top of it is where Machine Learning Engineers take over.
These four roles get lumped together constantly, but each one owns a different piece of the AI data stack. Here's where the boundaries actually sit:
A US data engineer costs $137,032 a year on average, with most salaries falling between $88,498 and $212,184.
By the numbers:
Demand keeps pushing those numbers higher. The data engineering services market is on track to grow from $104.49B in 2026 to $382.23B by 2034, a 17.6% CAGR, which is a big part of why qualified candidates are harder to land every year.
Direct hire gives you full ownership at the slowest, most expensive pace. A recruiter moves faster but vetting quality varies. Freelancers work for short projects but rarely own infrastructure long-term. Here's how all five options compare:
That last row is what data engineering staffing looks like in practice: a pre-vetted engineer joining your sprints and your Slack instead of a project being delivered and walked away from. Teams weighing a remote or offshore data engineer specifically can go deeper on that channel through hiring an Offshore AI Engineer.
Screen for the same core competencies regardless of channel:
These are the same competencies KDCI screens for before a candidate ever reaches your shortlist.
Every data engineer KDCI places completes an internal skills assessment before you ever see a profile. The assessment covers:
Pre-vetted means these five checks happen before a candidate enters your interview loop, not during it.
Start with a short brief: your stack, your requirements, and whether you need one data engineer or several. KDCI matches pre-vetted candidates against that brief and sends you a shortlist. You interview whoever fits, and your hire is typically onboarded within 7–14 days. Against the 62-day median for filling a technical role through a traditional search, that's a meaningfully faster path to the same capability.
At KDCI, we place data engineers who join your workflow and own your pipelines as if they built them from day one. They cost about a third less than a local hire and start in 7-14 days, pre-vetted for the exact infrastructure work your AI and ML systems depend on.
Tell us what your data stack looks like and where the gaps are, and we'll send you a shortlist of pre-vetted data engineers this week.
Look for experience with cloud data warehouses, ETL/ELT orchestration, batch and streaming pipelines, data quality, and lineage. For AI workloads, downstream ML or RAG experience is also valuable.
Ideally, yes. Experience with platforms such as Snowflake, BigQuery, or Redshift helps an engineer work independently within your existing data stack rather than requiring extensive platform-specific ramp-up.
It depends on where your bottleneck is. If your data is scattered, unreliable, or difficult to access, start with a data engineer; if your data is ready and the challenge is deploying or optimizing models, an ML engineer may be the better fit.
A dedicated data engineer should become part of your existing workflow, taking ownership of pipelines, data infrastructure, and quality processes while collaborating with the teams that depend on that data.
KDCI evaluates pipeline design, ETL/ELT tooling, cloud data warehouse experience, communication skills, English fluency, and timezone overlap before a candidate reaches your interview stage.
