Data Engineer vs MLOps Engineer: Salary & Career Benchmarks in Poland

In Poland, MLOps Engineer roles typically command a higher median salary than Data Engineer positions. According to our 2026 live benchmark data, a mid-level MLOps Engineer earns a median salary of PLN284K, whereas a Data Engineer brings in roughly PLN255K (a gap of PLN29K at the median).

Seniority, tech stack, and location all move this gap. Senior practitioners in either discipline can exceed the upper range through specialist skills. See the skills premium section below for the specific certifications and tools that push offers to the top of the range.

While both positions are vital to a modern tech organisation, Data Engineer and MLOps Engineer have fundamentally different daily workflows.

Data Engineer focuses primarily on designing, building, and maintaining scalable data pipelines and infrastructure. Day-to-day work revolves around writing Python or Scala, orchestrating workflows with Airflow or dbt, managing cloud data warehouses like BigQuery or Snowflake, and optimizing ingestion pipelines.

MLOps Engineer focuses on building and maintaining ML training and inference pipelines, model deployment workflows, and monitoring infrastructure using MLflow, Kubeflow, and Azure ML to productionise machine learning at scale. Their time is spent designing and maintaining ML training and inference pipelines, building CI/CD workflows for model deployment with MLflow, Kubeflow, and Azure ML, monitoring model performance and data drift in production, collaborating with data scientists to productionise experimental models, managing containerised ML workloads on Kubernetes, provisioning and optimising GPU infrastructure, and supporting model governance and auditability requirements.

Transitioning between these two paths is achievable but requires targeted upskilling.

Moving from Data Engineer to MLOps Engineer: Data engineers with ML interest, DevOps engineers who have worked with data science teams, and data scientists who want to specialise in production systems transition into MLOps roles.

Moving from MLOps Engineer to Data Engineer: Strong Python skills, SQL fluency, and comfort with cloud platforms (AWS, GCP, or Azure) are the primary entry points. Software engineers transitioning in find the shift is mostly domain knowledge rather than new fundamentals.

Neither path requires starting from scratch. Professionals in both roles share underlying technology fluency; the gap is usually domain knowledge and specific tooling rather than core engineering fundamentals.

In Poland in 2026, both roles are seeing demand, but with different drivers.

Data Engineer demand is extreme, particularly in Poland's banking and e-commerce sectors, which run some of the largest data platforms in Central Europe, led by employers such as mBank and ING Bank Śląski. MLOps Engineer demand is extreme, concentrated in Warsaw and Kraków product teams building AI-powered features, led by employers such as Google and Microsoft.

Workspace flexibility significantly impacts total compensation value in Poland.

Data Engineer roles score 78% on our remote-friendliness index (High). This is because pipeline and reporting work is largely asynchronous and tool-driven, a norm that Poland's IT sector has broadly kept in place thanks to the widespread B2B (umowa B2B) contracting model, which gives both employers and specialists more flexibility than a standard umowa o pracę. Where in-office attendance is required, it is typically driven by stakeholder reviews and cross-functional planning sessions, which larger employers with a Kraków or Wrocław global capability centre are more likely than smaller Polish software houses to formalise into a fixed hybrid schedule.

MLOps Engineer roles score 85% (Remote Friendly). Model integration and evaluation work is tool-driven and remote-friendly, a norm that Poland's IT sector has broadly kept in place thanks to the widespread B2B (umowa B2B) contracting model, which gives both employers and specialists more flexibility than a standard umowa o pracę is the primary driver of flexibility. When office days are required, it is usually for product alignment sessions and cross-functional AI roadmap planning, which larger employers with a Kraków or Wrocław global capability centre are more likely than smaller Polish software houses to formalise into a fixed hybrid schedule.