Analytics Engineer (dbt / SQL) vs Data Scientist (Risk & Compliance): Salary & Career Benchmarks in the Netherlands

In the Netherlands, Data Scientist (Risk & Compliance) roles typically command a higher median salary than Analytics Engineer (dbt / SQL) positions. According to our 2026 live benchmark data, a mid-level Data Scientist (Risk & Compliance) earns a median salary of €75K, whereas a Analytics Engineer (dbt / SQL) brings in roughly €71K (a gap of €4K 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, Analytics Engineer (dbt / SQL) and Data Scientist (Risk & Compliance) have fundamentally different daily workflows.

Analytics Engineer (dbt / SQL) focuses primarily on transforming raw data into trusted, business-ready datasets using dbt modelling layers and semantic logic. Day-to-day work revolves around writing and testing dbt models, maintaining data catalogues, defining shared metric logic with analysts, and enforcing data quality checks across the warehouse.

Data Scientist (Risk & Compliance) focuses on building statistical and machine-learning models for credit risk, fraud detection, and regulatory compliance monitoring within a bank or fintech. Their time is spent building and validating credit-risk or fraud-detection models, running model performance monitoring, documenting models for regulatory review, and working with compliance teams on model explainability requirements.

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

Moving from Analytics Engineer (dbt / SQL) to Data Scientist (Risk & Compliance):

Moving from Data Scientist (Risk & Compliance) to Analytics Engineer (dbt / SQL): Data analysts with strong SQL and a working knowledge of dbt are the most natural fit, since the role sits at the intersection of engineering and analysis.

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 the Netherlands in 2026, both roles are seeing demand, but with different drivers.

Analytics Engineer (dbt / SQL) demand is very high, particularly in Data platform teams inside Amsterdam scale-ups and financial institutions, led by employers such as Adyen and ING, with Belastingdienst's own data infrastructure programme adding public-sector demand. Data Scientist (Risk & Compliance) demand is very high, concentrated in Dutch banks and fintechs in Amsterdam building risk and fraud models under DNB and AFM supervisory expectations, led by employers such as ING and Adyen.

Workspace flexibility significantly impacts total compensation value in the Netherlands.

Analytics Engineer (dbt / SQL) roles score 68% on our remote-friendliness index (High). This is because model-building and data-pipeline work is largely asynchronous and tool-driven, which fits Dutch employers' well-established hybrid-work norms. Where in-office attendance is required, it is typically driven by cross-functional data modelling discussions and stakeholder alignment sessions, which Amsterdam employers generally keep to one or two fixed office days.

Data Scientist (Risk & Compliance) roles score 45% (Moderate). Model development work can largely be done remotely, and Dutch banks' flexible-working norms keep this role hybrid by default is the primary driver of flexibility. When office days are required, it is usually for model validation sign-off and regulator-facing documentation keep this role largely on-site at DNB-regulated banks in Amsterdam.