Own enterprise data quality standards and monitoring across source systems, ETL pipelines, and cloud warehouses. Profile and validate data, perform root-cause analysis, design ETL test strategies, and prevent bad data from reaching models and dashboards. Partner with Data Engineering, Data Science, and Governance to define validation criteria, report KPIs, and mentor junior analysts.
Role: Quality Engineer
Location: New York, NY
We are looking for an experienced Data Quality Engineer with strong expertise in cloud-based data platforms, data pipeline testing, API automation, performance testing, and database validation. The ideal candidate will be responsible for ensuring the quality, reliability, accuracy, and performance of large-scale data platforms and pipelines, with a strong focus on GCP and Python-based test automation.
Key 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, including data ingestion, transformation, processing, storage, and downstream consumption.
- Perform data validation across streaming and batch-processing pipelines, ensuring data accuracy, completeness, consistency, and integrity.
- Develop and maintain Python-based automation frameworks for backend, REST API, and data validation testing.
- Develop UI automation tests using Playwright and perform manual validation where required.
- Perform RESTful API testing, including functional, integration, negative, regression, and end-to-end validation.
- Design and execute performance, load, stress, latency, and throughput testing using tools such as k6.
- Validate data across relational, document, analytical, and columnar databases, including PostgreSQL/AlloyDB, MongoDB, BigQuery, and optionally ClickHouse.
- Work extensively within the Google Cloud Platform (GCP) ecosystem, including Pub/Sub, GCS, Dataflow, Dataproc, and Cloud Composer.
- Troubleshoot data discrepancies, pipeline failures, API issues, and performance bottlenecks, collaborating closely with Data Engineering, Development, DevOps, and Product teams.
- Build automated reconciliation and data-validation checks to identify missing, duplicate, inconsistent, incorrect, or delayed data.
- Contribute to CI/CD processes by integrating automated data, API, and application quality checks into deployment pipelines.
- Support data profiling, governance, cataloging, lineage, and automated data-quality controls, preferably using Dataplex.
- Strong experience as a Data Quality Engineer, SDET, Data Test Engineer, or QA Automation Engineer working with modern data platforms.
- Strong hands-on experience with Python-based test automation.
- Strong experience testing RESTful APIs and backend services.
- Experience with Playwright or similar UI automation frameworks.
- Hands-on experience with GCP data technologies, particularly Pub/Sub, GCS, Dataflow, Dataproc, Cloud Composer, and BigQuery.
- Strong SQL and data-validation skills with experience working across large and complex datasets.
- Experience with PostgreSQL/AlloyDB, MongoDB, and BigQuery.
- Hands-on performance testing experience using k6 or comparable modern load-testing tools.
- Strong understanding of ETL/ELT, batch and streaming pipelines, data reconciliation, schema validation, and data-quality principles.
- Strong analytical, debugging, problem-solving, and cross-functional communication skills.
- 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 tests into CI/CD pipelines.
- Knowledge of data observability, data lineage, metadata management, and cloud-native monitoring
TechBlocks New York, New York, USA Office
165 Broadway, 23rd Floor, New York, City ,, New York, New York, United States, 10006
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