At Lansweeper, we know our greatest asset is our data. We’re building scalable, intelligent system and to do that, we need data that’s accurate, reliable, and trusted. We’re looking for a Data Quality Automation Engineer to help us ensure the integrity of our machine learning pipelines and the health of the data that fuels our products. Are you passionate about automating data validation, monitoring model inputs and outputs, and defining what “good” data really means? If so, read on.
Are you passionate about automating data validation, monitoring model inputs and outputs, and defining what “good” data really means? If so, read on.

• Design and implement automated data quality checks across ML pipelines to validate training data, features, and real-time inference inputs
• Build and maintain data validation frameworks using Python, SQL, and tools like Great Expectations, dbt, or Apache Deequ
• Develop profiling and monitoring systems for structured and unstructured data used in AI workflows
• Create drift detection and anomaly alerting pipelines to surface issues before they impact model performance
• Integrate data validation steps into CI/CD workflows using tools like Airflow, MLflow, or Kubeflow
• Collaborate with ML engineers, data scientists, and analysts to define data quality KPIs and enforce governance policies
• Set up dashboards, alerts, and reports to ensure data quality is visible and actionable across teams
• Drive data quality strategy and help shape data reliability engineering practices in a fast-paced AI-driven environment