Data Quality Automation Engineer

Context & Impact

Lansweeper’s growth in Asset Intelligence means our systems rely on complete, accurate, and trusted data. With the Redjack acquisition, we’re increasing our asset visibility with on-prem network sensors. We’re working on building scalable, intelligent pipelines that power our next generation of insights.

To accelerate this transformation, we’re hiring a Data Quality Automation Engineer within our Quality Engineering (QE) Team. You'll help build out automated test systems for Lansweeper’s and Redjack’s integrated Data/ML pipelines, design test plans alongside the team and help test the product, ensuring integrity and reliability.

Your Goals

• Implement test frameworks across RedJack’s infrastructure as it integrates with Lansweeper’s architecture.
• Implement data quality test frameworks across combined data pipelines (ML and analytics).
• Automated e2e & regression testing within CI/CD pipelines.

Challenge

The Main Challenges You’ll Face Are

• Ensuring smooth integration of Redjack’s data pipelines with Lansweeper’s systems.
• Testing deployments of network sensors to a variety of IT environments.
• Scaling automated data quality checks across hybrid data environments.
• Embedding data validation and testing into CI/CD pipelines to safeguard model and product reliability.

Key Responsibilities

• Work with the development team to continuously deliver high quality software to production.
• Participate in test planning and cross‑team QA efforts for data products.
• Maintain and write e2e automated test scripts for our CI/CD workflows (CircleCI, Github actions, etc).
• Deploying and testing Network sensors to various platforms (Linux, Windows, etc) and various IT environments (TAP, SPAN, ERSPAN, NETFLOW, etc).
• Set up monitoring dashboards, alerts, and anomaly detection pipelines for proactive issue management.
• Document and evolve testing strategies for data validation, profiling, and pipeline reliability.
• Design and implement automated data quality test plans for structured and unstructured data within machine learning pipelines.

Job Requirements

Hard Skills

• 4+ years in Quality Engineering, Data Quality, or ML Test Automation roles.
• Strong proficiency in Python and SQL for building validation and monitoring tools.
• Skilled in automating tests within CI/CD (Airflow, Kubeflow, MLflow, Github Actions).
• Experience with data quality frameworks (Great Expectations, dbt, Apache Deequ).
• Experience in testing distributed systems (API validation, Kafka, etc).
• Experience with cloud‑based data infrastructure (Snowflake, BigQuery, AWS S3).

Nice To Haves

• Experience with Rust.
• Familiarity with mocking libraries (Mockito, mountebank, etc).
• Familiarity with Docker & Kubernetes.
• Familiarity with Networking concepts.
• Experience in AWS, GCP, and Azure.

Soft Skills

• Analytical mindset with strong problem‑solving capability.
• Excellent communication and cross‑team collaboration.
• Detail‑oriented, structured, and committed to continuous improvement.

Our Offer

• Competitive salary according to industry benchmarks.
• Benefits: comprehensive health insurance, meal vouchers, pension plan, company car, Flexible Income Plan, phone subscription…
• Career growth & learning opportunities within a fast‑scaling SaaS company.
• Flexibility in working hours and hybrid work options.
• Engaging company culture with team events and international collaboration.

Key skills/competency

• Data Quality Automation
• ML Test Automation
• Python
• SQL
• CI/CD
• Distributed Systems Testing
• Cloud Data Infrastructure
• Data Validation
• Monitoring & Alerting
• Problem-solving