Full Description

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.

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.