Quality Senior Engineer - Data

Requirements

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or equivalent practical experience.
• 4-8 years of experience in technical software testing, QA automation, or data reliability engineering roles.
• Deep proficiency in Python, SQL (Advanced), and Java or JavaScript.
• Experience working with cloud infrastructure (GCP / AWS / Azure) and workflow orchestration tools (e.g., Airflow).
• Strong hands-on experience testing REST APIs, backend databases, enterprise integrations, and complex data models.
• Exceptional data investigation and analytical skills, with a proven ability to isolate patterns of failure in messy data sets.
• Standout communication skills with a track record of effective stakeholder management, cross-functional collaboration, and ownership of delivery timelines.
• Familiarity with data observability concepts, data governance, and automated incident management frameworks.
• Experience in the Digital Marketing or Ads ecosystem (campaign metrics, ad serving, or digital media planning analytics).
• Natural leadership acumen with experience managing inflow queues, prioritizing test technical debt, and mentoring junior engineers.

Benefits

Additional Information

Join Us! At Google Operations Center we help Google users and customers solve problems and achieve their goals-all while enjoying a culture focused on improving continuously and being better together. We work hard, we play hard, and we want you to join us! A Senior Software Quality Engineer within the Google Operations Center (GOC) is responsible for ensuring the reliability and quality of technical solutions by designing and maintaining robust, scalable automation frameworks. This role drives a quality-first culture by integrating automated tests into the CI/CD pipeline and collaborating across development teams to define strategy and acceptance criteria. As part of a centralized maintenance model, you will play a critical role in promoting consistent investment and long-term reliability across the entire gTech Ads portfolio. Position Responsibilities: Data Automation Framework Architecture: Design, develop, and scale robust, reusable test automation frameworks using Java, Python, and JavaScript to validate data ingestion, transformation, and storage layers. End-to-End Data Pipeline Testing: Architect and execute automated testing for complex ETL/ELT pipelines, validating data synchronization, schema evolution, and data transformations across BigQuery, NoSQL databases, or Cloud Warehouses. Data Quality & Logic Verification: Implement automated data-diffing, data profiling, and statistical data quality checks using specialized tools like dbt-tests, Great Expectations, or custom Python data validation libraries to monitor data health. CI/CD Integration: Seamlessly embed automated data quality checks, API tests, and regression suites into modern CI/CD pipelines using GitLab CI, GitHub Actions, or Jenkins to enable continuous, automated testing upon every code check-in. API & UI Validation: Select and deploy optimal tooling (e.g., Postman, RestAssured, Playwright, or Selenium) to test upstream data-producing APIs and downstream data-consuming web applications (HTML5, XML). Cross-Functional Strategy: Collaborate closely with Data Engineers, Product Owners, and Analytics Managers to review technical design documents, formulate comprehensive test strategies, and map business requirements to automated data assertions. Root Cause & Defect Analysis: Conduct deep-dive investigations and root-cause analysis on pipeline failures, data anomalies, and logic regressions. Write advanced SQL queries to isolate data corruption issues and partner with engineering to deploy fixes. Metrics, Observability & Reporting: Define, track, and visualize data quality metrics (such as data freshness, lineage completeness, and defect density) by building automated dashboards using Looker, Tableau, or PLX/DataStudio.

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