We are seeking an experienced Quality Engineer / Data Quality Analyst for a 6-month contract position based in Manhattan, New York. This hybrid role requires 2 days per week on-site. 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