Description

You will work closely with Data Engineering, Development, DevOps, and Product teams to design and execute comprehensive data quality and testing strategies for cloud-based data platforms, ensuring the accuracy, reliability, completeness, consistency, and performance of enterprise data pipelines and applications in the automotive industry.

Responsibilities

• Design and execute comprehensive data quality and testing strategies for cloud-based data platforms, pipelines, APIs, and applications.
• Validate end-to-end data pipelines covering data ingestion, transformation, processing, storage, and downstream consumption.
• Perform data validation across batch and streaming pipelines, ensuring accuracy, completeness, consistency, and integrity.
• Develop and maintain Python-based automation frameworks for backend, REST API, database, and data-validation testing.
• Develop and maintain UI automation tests using Playwright or Cypress and perform manual validation when required.
• Perform comprehensive REST API testing, including functional, integration, negative, regression, and end-to-end testing.
• Design and execute performance, load, stress, latency, and throughput testing using k6 or comparable tools.
• Validate data across relational, document, analytical, and columnar databases.
• Work extensively with the GCP ecosystem, including Pub/Sub, Google Cloud Storage (GCS), Dataflow, Dataproc, Cloud Composer, and BigQuery.
• Perform database validation using PostgreSQL/AlloyDB, MongoDB, and BigQuery.
• Troubleshoot data discrepancies, pipeline failures, API issues, and performance bottlenecks.
• Build automated data reconciliation and validation checks to identify missing, duplicate, inconsistent, incorrect, or delayed data.
• Collaborate with Data Engineering, Development, DevOps, and Product teams to investigate and resolve data-quality issues.
• Integrate automated data, API, and application quality checks into CI/CD pipelines.
• Support data profiling, governance, cataloging, lineage, and automated data-quality controls.
• Contribute to continuous improvement of data-quality frameworks, automation, testing standards, and processes.
• Document test scenarios, validation results, defects, root causes, and resolutions.

Qualifications

• Required
  • Proven experience as a Data Quality Engineer, Data Test Engineer, SDET, QA Automation Engineer, or similar role.
  • Strong hands-on experience with Python-based test automation.
  • Strong experience testing RESTful APIs and backend services.
  • Hands-on experience with Playwright or Cypress.
  • Strong experience with GCP data technologies, particularly Pub/Sub, GCS, Dataflow, Dataproc, Cloud Composer, and BigQuery.
  • Strong SQL and data-validation skills with experience working with large and complex datasets.
  • Experience with PostgreSQL/AlloyDB, MongoDB, and BigQuery.
  • Hands-on experience with performance/load testing, preferably using k6 or similar tools.
  • Strong understanding of ETL/ELT, batch and streaming pipelines, data reconciliation, schema validation, data integrity, and data-quality principles.
  • Strong analytical, debugging, problem-solving, and communication skills.
• Preferred
  • Experience with ClickHouse or other high-performance columnar databases.
  • Experience with Dataplex for data profiling, cataloging, governance, and automated data-quality controls.
  • Experience integrating automated testing into CI/CD pipelines.
  • Knowledge of data observability, data lineage, metadata management, and cloud-native monitoring.
  • Experience working in the automotive, automotive technology, dealership, or transportation

Key Skills

Education

Any Graduate