Data Quality Engineer — CI/CD, Observability & Dashboards

Dynata; United States; Remote; USD 100,000 - 140,000; Full time

Benefits offered by this job

Competitive salary

Inclusive work environment

Global collaboration

Job summary

Dynata is seeking a Data Quality Engineer to design and implement a enterprise-wide data quality framework for our lakehouse platform. You will build automated validation, monitoring, and anomaly detection, and integrate quality checks into CI/CD pipelines to prevent bad data from reaching production.

The role requires hands-on experience with data quality tools, strong SQL/Python skills, and experience with Spark or dbt.

Qualifications

• 2-4 years in data engineering, data quality, or analytics engineering roles.
• Hands-on experience with data quality tools such as Great Expectations, dbt tests, Soda, Monte Carlo, or similar.
• Strong SQL and Python skills for validation logic and quality metric queries.
• Experience with Apache Spark or dbt for large-scale data transformation and testing.
• Familiarity with data profiling techniques and anomaly detection.
• Knowledge of data observability concepts in lakehouse environments.
• Experience with Airflow or similar orchestration tools.

Responsibilities

• Design and implement a comprehensive data quality framework covering completeness, accuracy, consistency, timeliness, and uniqueness.
• Build automated data quality test suites using Great Expectations, dbt tests, or Soda Core.
• Develop data quality dashboards and scorecards for data producers and consumers.
• Implement anomaly detection and data observability solutions.
• Integrate quality checks into CI/CD pipelines to prevent bad data from production.

Skills

SQL

Python

Airflow

Spark

dbt

Education

Bachelor's degree in Computer Science/Statistics/Engineering

Tools

Great Expectations

dbt tests

Soda Core

Monte Carlo

Bigeye

Airflow

Databricks

Snowflake

DataHub

Job description

Dynata is seeking a Data Quality Engineer to design and implement a enterprise-wide data quality framework for our lakehouse platform. You will build automated validation, monitoring, and anomaly detection, and integrate quality checks into CI/CD pipelines to prevent bad data from reaching production.

The role requires hands-on experience with data quality tools, strong SQL/Python skills, and experience with Spark or dbt.