QA Lead Data Engineering
Listed on 2026-07-01
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IT/Tech
Data Engineering, Data Analyst, IT QA Tester / Automation, Cloud Computing: Infrastructure & Operations
QA Lead – Data Engineering Pasadena, CA
Long Term Contract
We are seeking a highly skilled QA Lead – Data Engineering to define and implement robust QA strategies, automated testing frameworks, and data quality assurance processes. The ideal candidate will have expertise in Google Cloud Platform (GCP), Databricks, ETL pipelines, SQL, Python scripting, and CI/CD workflows. This role requires a strong understanding of data governance, data validation, and test automation to ensure high-quality data engineering solutions.
QAStrategy and Leadership:
Define and implement robust QA strategies, methodologies, and processes for data engineering projects.
Lead and mentor a team of QA engineers, ensuring alignment with best practices and project goals.
Collaborate with stakeholders to understand data quality requirements and translate them into test plans and cases.
Testing Frameworks and Automation:Develop and maintain automated testing frameworks for ETL pipelines, data lakes, and data warehouses on Google cloud platform, Databricks, and related services.
Design data validation and verification processes to ensure the accuracy and consistency of data across pipelines.
Automate regression, performance, and integration testing to minimize manual efforts.
Data Quality Assurance:Conduct root cause analysis for 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 tools like Google, Power BI, or custom solutions.
Technical Skills andQualifications:
GCP Expertise:
Proficient in GCP, Dataflow, Big Query, Cloud composer, and Google Storage services.
Databricks:
Experience with Spark-based data engineering workflows, including Delta Lake.
Testing Tools & Frameworks:
Strong experience with tools like PyTest, dbt (data build tool), or similar testing frameworks for data pipelines.
SQL and Scripting:
Advanced SQL skills for data validation with Python proficiency for automation.
Big data Knowledge:
Knowledge of Big Data processing and distributed computing. Understand the importance of data modeling concepts.
CI/CD:
Knowledge of CI/CD pipelines on or Git Hub Actions, focusing on data engineering workflows.
Data Governance:
Familiarity with data governance tools and concepts such as metadata management, lineage, and data cataloging.
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