Data Quality Analyst

Rootshell Enterprise Technologies • Pleasanton, CA

2d • Onsite

About The Position

Rootshell Inc. is seeking a Data Quality Analyst for a client. This onsite role in Pleasanton, California, focuses on quality assurance for data engineering projects. The analyst will define QA strategies and processes, lead and mentor QA engineers, and work with stakeholders to turn data quality requirements into test plans and cases.

The role involves developing and maintaining automated testing frameworks for ETL pipelines, data lakes, and data warehouses on Google Cloud Platform and Databricks. It includes designing data validation and verification processes; automating regression, performance, and integration testing; investigating data quality issues and driving corrective actions; supporting data governance and security practices; and developing profiling and monitoring dashboards.

Requirements

• Proficiency with GCP, including Dataflow, BigQuery, Cloud Composer, and Google Storage services.
• Experience with Databricks and Spark-based data engineering workflows, including Delta Lake.
• Strong experience with PyTest, dbt, or similar testing frameworks for data pipelines.
• Advanced SQL skills for data validation.
• Python proficiency for automation.
• Knowledge of big data processing and distributed computing.
• Understanding of data modeling concepts.
• Knowledge of CI/CD pipelines and GitHub Actions, with a focus on data engineering workflows.
• Familiarity with data governance concepts and tools, including metadata management, lineage, and data cataloging.
• Excellent communication skills, including the ability to explain complex problems to varied audiences, including executives.

Responsibilities

• Define and implement QA strategies, methodologies, and processes for data engineering projects.
• Lead and mentor QA engineers, aligning their work with best practices and project goals.
• Collaborate with stakeholders to translate data quality requirements into test plans and cases.
• Develop and maintain automated testing frameworks for ETL pipelines, data lakes, and data warehouses using GCP, Databricks, and related services.
• Design data validation and verification processes to ensure accuracy and consistency across data pipelines.
• Automate regression, performance, and integration testing.
• Conduct root cause analysis of data quality issues and drive corrective actions.
• Ensure compliance with data governance policies and data security best practices.
• Develop data profiling and monitoring dashboards using Google Monitor, Power BI, or custom solutions.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed