Job Description

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.

A Day in the life of a Data Quality Automation Engineer

· You design and implement automated data quality checks to validate features, training sets, and inference inputs across ML pipelines

· You collaborate with ML engineers and data scientists to define quality KPIs and implement safeguards for data integrity and model health

· You develop and maintain data validation frameworks using tools like Great Expectations, dbt, or custom Python/SQL scripts

· You integrate data quality checks into CI/CD workflows for ML pipelines using tools such as Airflow, MLflow, or Kubeflow

· You build and monitor anomaly detection and drift monitoring systems for real-time and batch data

· You create dashboards and automated alerts to provide visibility and early warning signals on data issues

· You participate in code reviews, agile ceremonies, and data governance discussions to ensure quality is a shared responsibility

· You grow your skillset by exploring innovations in data observability, AI governance, and quality engineering at scale

What you will be doing

· 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 workflow s 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

Are you the new Data Quality Automation Engineer at Lansweeper?

· You have 3+ years of experience in data engineering, data quality, or ML platform roles, with a strong focus on automated validation and observability 

· Proficiency in Python and SQL, and familiarity with tools like dbt, Great Expectations, Apache Deequ, or similar

· Experience building data validation workflows integrated into CI/CD pipelines

· Understanding of machine learning workflows, including training, serving, and feature engineering pipelines

· Familiarity with drift detection, anomaly monitoring, and model data health tracking

· Experience with cloud-based data infrastructure (e.g., Snowflake, BigQuery, S3) and workflow orchestration (Airflow, Dagster)

· You’re comfortable working in cross-functional teams and partnering with stakeholders to define data quality SLAs

Nice to haves

· Experience with  data observability platforms (e.g., Monte Carlo, Soda, Datafold)

· Knowledge of  data governance practices and collaboration with compliance or data stewardship teams

· Familiarity with  alerting and monitoring tools like Prometheus, Grafana, or Slack/email-based custom alerting

· Contributions to internal frameworks or open-source libraries for data quality

What we offer

· A key role in shaping how we monitor and automate data quality in production AI systems

· An open, flexible work environment with a strong technical culture

· The opportunity to work with cutting-edge data and ML platforms at scale

· Competitive salary, equity opportunities, and a supportive, growth-oriented team

· Ongoing learning opportunities across data engineering, ML infrastructure, and AI reliability

This role may include participation in our on-call duty system, which means being available outside regular hours to respond to urgent technical incidents. On-call weeks are planned in advance, and we offer a base allowance plus potential overtime compensation. You would need to respond within 30 minutes and be reachable by laptop, phone, or email when on call.

US: Diversity Statement – Equal Employment Opportunity

It is Lansweeper’s policy to provide equal employment opportunity to all applicants and employees. Lansweeper disapproves of, and will not tolerate, unlawful discrimination against any applicant or employee because of race, color, national origin or ancestry, gender (including pregnancy, childbirth, or related medical conditions), gender identity, age, religion, disability, family care status, veteran status, marital status, sexual orientation, or any other basis protected by local, state, or federal laws.

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