Hire pre-vetted Data Engineers

Data science models are only as good as the data feeding them. And data doesn't clean, transform, and deliver itself. That's what a data engineer does, and finding a good one is arguably harder than finding a data scientist, because the role is less standardized and the tooling landscape changes every year.

The data engineers available on Cortance have hands-on experience building the infrastructure that production ML systems depend on. We're not talking about people who can spin up an Airflow DAG from a tutorial. These are engineers who've built real-time data ingestion from drone camera feeds, managed petabyte-scale geospatial datasets across Google Cloud and AWS, set up MLOps with Kubeflow for automated model retraining, and designed document processing systems that convert unstructured PDFs to structured JSON at scale.

Every engineer on this platform has been verified through real commercial project delivery — not self-reported skills or recruiter interviews. Cortance has made 150+ successful placements with a 94% retention rate. The data engineers here average 6 years of hands-on experience, and their profiles show exactly which tools they've used, for how long, and in which production contexts. That level of transparency is what makes the difference between hiring someone who looks right on paper and someone who actually ships.

Core capabilities

Python-based data processing with Pandas, NumPy, and SciPy. SQL across PostgreSQL, MySQL, BigQuery, and ClickHouse. Cloud infrastructure on GCP (Vertex AI, BigQuery, Cloud Storage) and AWS (S3, EC2, Lambda). Containerization with Docker and docker-compose. CI/CD setup and maintenance. Data versioning with DVC. Experiment tracking with MLflow and Weights & Biases.

Pipeline orchestration is where many data engineering hires fall short. The engineers here have worked with Airflow, Prefect, and Kubeflow in production — not in sandboxes. They understand how orchestration breaks under real data volumes, how to design for failure recovery, and how to build monitoring that catches pipeline degradation before it reaches the model layer. That operational depth is what separates a data engineer who can build a pipeline from one who can own it long-term.

What kinds of projects they handle

The strength of a good data engineer is adaptability across domains. The same person who builds a real-time telemetry ingestion system can set up a document processing workflow or design a geospatial data lake. What matters is the engineering: reliable data processing at scale, clean orchestration, proper monitoring, and infrastructure that doesn't break when data volumes grow 10x. The data engineers on Cortance have worked across enough different industries and data types that they can ramp up on your specific domain quickly.

The most common issue we see when companies bring in a data engineer from a generic platform: the person handles the initial build well but hasn't designed for what happens when data volume triples or the upstream source changes format mid-pipeline. Production data infrastructure fails in specific, predictable ways, and engineers who've operated it at scale know where those failure points are before they become incidents.

How hiring works

Submit your requirements through the platform, fill a short questionnaire, or reach out to a hiring manager directly. Cortance's AI-powered matching system scores candidates against your stack, domain, cloud environment, and seniority level — and delivers a curated shortlist of matched data engineers within 30 minutes. Most clients complete the hire in 2 days. Cortance handles contracts, payroll, and onboarding. If a match doesn't work out within the first 2 weeks, a replacement is arranged at no extra cost.

Freelance data engineers, remote from Europe

All data engineers on Cortance work remotely. Based in Ukraine, Portugal, Spain, and Georgia. Comfortable with English-language communication, distributed teams, and async workflows. Available for freelance, contract, or dedicated roles. You can review their full project histories, see exactly which tools they've used and for how long, and reach out directly.

If you need to hire a remote data engineer who already knows how to build the infrastructure ML teams depend on, check the profiles ahead.

Frequently Asked Questions

1. Can I hire a data engineer who also does ML work? Yes. Several data engineers on Cortance have worked alongside data science teams and understand how ML systems are structured. They can build the data layer, set up feature stores, manage model serving infrastructure, and handle MLOps, even though model training itself sits with data scientists. But if you build a really large-scale production infrastructure, it's better to dedicate a data engineer who focused to engineering work.
2. What cloud platforms do your data engineers work with? Primarily Google Cloud Platform and AWS. Some have Azure experience as well. Specific services (BigQuery, Vertex AI, S3, Lambda) are listed per profile.
3. Do you have data engineers with geospatial or drone data experience? Yes, this is a strong niche. Multiple engineers have years of experience processing large-scale geospatial datasets with tools like Rasterio, GDAL, GeoPandas, and Shapely.
4. What if I need both a data engineer and a data scientist? Cortance covers both roles. If you're building an end-to-end ML system, you can match a data engineer for the pipeline and infrastructure layer and a data scientist for modelling and analysis through the same platform, with the same verification standard applied to both.