Platform Data Engineer: IoT, Time-Series & ML for Climate

Quintall; Maasdijk; On-site; EUR 50,000 - 67,000; Full time

Benefits offered by this job

29 days annual leave

8% holiday pay

Ownership of platform architecture

Direct impact on greenhouse tech

Job summary

Ridder is seeking a Data Engineer to own the complete data lifecycle for our climate control platform powering greenhouse decisions worldwide. You will design scalable pipelines, ensure data quality, and enable data scientists and engineers with trusted datasets.

This role focuses on building a robust data foundation rather than dashboards, with ownership of data contracts, time-series data, and production ML workflows in a cloud-native environment.

Qualifications

• 5+ years of experience building production-grade data platforms and pipelines.
• Strong Python and SQL skills.
• Experience with time-series, IoT, or high-volume data environments.
• Experience with cloud platforms, containers, CI/CD, and infrastructure as code.
• Ownership mindset with focus on reliability and quality.
• Experience operating ML models in production: training pipelines, registries, monitoring.

Responsibilities

• Design and operate scalable data pipelines and time-series data platforms.
• Define data contracts, quality standards, and versioned schemas across the platform.
• Enable data scientists and software engineers with trusted datasets and feature pipelines.
• Help build and grow the data engineering capability within the team.
• Run the models in production: scheduled training, batch inference, model registry, deployment and monitoring.
• Orchestrate the platform's scheduled jobs — ingestion, validation, training data, model runs, evaluation

Tools

Dagster

Airflow

Prefect

Job description

Ridder is seeking a Data Engineer to own the complete data lifecycle for our climate control platform powering greenhouse decisions worldwide. You will design scalable pipelines, ensure data quality, and enable data scientists and engineers with trusted datasets.

This role focuses on building a robust data foundation rather than dashboards, with ownership of data contracts, time-series data, and production ML workflows in a cloud-native environment.