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 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.