Data Warehouse / Data Lake Tester – GCP

🚀 Data Warehouse / Data Lake Tester – GCP
Job Summary
We are looking for an experienced Data Warehouse / Data Lake Tester with mandatory hands-on experience testing Data Lake solutions within Google Cloud Platform (GCP).
The ideal candidate must have strong experience validating back-end data across large-scale Data Lake, Data Warehouse, or enterprise data platform implementations, with a particular focus on GCP-based data environments.
This role requires advanced SQL skills, strong knowledge of data warehousing and Data Lake concepts, and the ability to understand complex data architectures and business requirements. The successful candidate will design complex data test scenarios, validate data across different layers and pipelines, identify discrepancies, and actively participate in data issue investigation and resolution.
Key Responsibilities
Perform comprehensive back-end data testing within GCP-based Data Lake environments.
Test and validate large-scale Data Lake, Data Warehouse, and enterprise data platform implementations.
Validate data ingestion, transformation, processing, storage, and downstream consumption across GCP data environments.
Use SQL extensively to validate data accuracy, completeness, consistency, integrity, and business rules.
Design and execute complex test cases based on data architecture, technical specifications, data flows, and business requirements.
Validate data transformations, mappings, dependencies, and processing logic.
Perform data reconciliation and identify discrepancies across different layers of the data platform.
Investigate complex data issues and perform root-cause analysis.
Assist with and lead data issue investigation and resolution efforts in collaboration with Data Engineering, Development, QA, and business teams.
Participate throughout the Software Development Life Cycle (SDLC) and ensure appropriate testing coverage.
Document and communicate defects, data quality issues, risks, and testing results to technical and business stakeholders.
Must-Have Qualifications
Hands-on experience testing Data Lake solutions on Google Cloud Platform (GCP) – REQUIRED.
Strong professional experience with back-end data testing.
Experience testing large-scale Data Lake, Data Warehouse, or enterprise data platform implementations.
Strong understanding of GCP data architectures and data processing workflows.
Advanced hands-on SQL skills for data validation, reconciliation, analysis, and troubleshooting.
Strong understanding of Data Lake and Data Warehousing concepts and architectures.
Experience validating data ingestion, transformations, data pipelines, and downstream outputs.
Experience translating business and technical requirements into complex data test cases and scenarios.
Ability to understand complex system architecture, data flows, dependencies, mappings, and business rules.
Experience investigating data discrepancies, performing root-cause analysis, and supporting or leading data issue resolution.
Solid understanding of the SDLC and QA methodologies.
Strong analytical, troubleshooting, and problem-solving skills.
Strong communication skills and ability to collaborate with technical and business stakeholders.
Relevant GCP Experience
Candidates should have practical experience working with GCP Data Lake/data platform environments. Experience with technologies such as the following is highly relevant:
Google Cloud Storage (GCS)
BigQuery
Data ingestion and transformation pipelines on GCP
GCP-based ETL/ELT workflows
Data processing and orchestration services
Data validation and reconciliation across GCP data layers
Important: General GCP exposure alone is not sufficient. Candidates must have actual experience working with or testing Data Lake/data platform implementations on GCP.
Nice-to-Have Qualifications
Strong knowledge of Data Quality concepts, including completeness, accuracy, consistency, validity, integrity, and reconciliation.
Experience performing source-to-target data validation.
Experience testing complex ETL/ELT pipelines.
Experience with high-volume or high-scale datasets.
Experience with automated data testing frameworks or tools.
Experience working closely with Data Engineers and Data Architects.

Work arrangement
Yes