Data Quality Automation Engineer, Mid-Level

Jobright.ai; Austin (TX); On-site; USD 70,000 - 100,000; Full time

Benefits offered by this job

Medical insurance

Vision insurance

401(k)

Job summary

A technology solutions provider is seeking a Mid-Level Data Quality Automation Engineer to ensure data integrity across ML pipelines. The role involves designing automated checks, developing validation frameworks, and collaborating with teams to maintain data quality. The ideal candidate has over 3 years of experience in data engineering with proficiency in Python and SQL, and familiarity with tools like dbt and Great Expectations. This position is based in Austin, Texas.

Qualifications

• 3+ years of experience in data engineering, quality, or ML.
• Strong focus on automated validation and observability.
• Experience in building data validation workflows integrated into CI/CD.

Responsibilities

• Design and implement automated data quality checks.
• Collaborate to define quality KPIs and safeguards.
• Build dashboards for visibility on data issues.

Skills

Python

SQL

Data quality validation

Automated testing

Machine learning workflows

Education

3+ years of experience in data engineering or data quality

Tools

dbt

Great Expectations

Apache Deequ

Airflow

Job description

Data Quality Automation Engineer, Mid-Level

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Data Quality Automation Engineer, Mid-Level

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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

Qualifications:

Required:

• 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

Preferred:

• 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

Company:

Lansweeper develops a SaaS solution providing organization with a single pane of glass across their techology estate. Founded in 2004, the company is headquartered in Grembergen, Oost-Vlaanderen, BEL, with a team of 201-500 employees. The company is currently Growth Stage.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Industries

  Software Development

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Inferred from the description for this job

Medical insurance

Vision insurance

401(k)

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