Data Quality Automation Engineer, Mid-Level
Jobright.ai
Data Quality Automation Engineer, Mid-Level Join to apply for the Data Quality Automation Engineer, Mid-Level role at Jobright.ai Data Quality Automation Engineer, Mid-Level 2 days ago Be among the first 25 applicants Join to apply for the Data Quality Automation Engineer, Mid-Level role at Jobright.ai Get AI-powered advice on this job and more exclusive features. Jobright is an AI-powered career platform that helps job seekers discover the top opportunities in the US. We are NOT a staffing agency. Jobright does not hire directly for these positions. We connect you with verified openings from employers you can trust. Job Summary: Lansweeper is focused on ensuring data integrity for their machine learning pipelines. They are seeking a Data Quality Automation Engineer to design and implement automated data quality checks, collaborate with teams to define quality KPIs, and develop frameworks to maintain data validation across various workflows. Responsibilities:
- 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
- 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
- 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
- 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
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