Data Lake Tester - Compu-Vision Consulting : Job Details

Title: Data Lake Tester Contract Length: 06 months Location: Remote Job Description:

• A Data Lake Tester plays a crucial role in ensuring the integrity, performance, and reliability of data stored in a data lake. Here are the key responsibilities and roles of a Data Lake Tester: A Data Lake Tester ensures that the data lake is robust, reliable, and ready to support data-driven decision-making and analytics.

Data Validation and Verification:

• Ensuring the accuracy and completeness of data ingested into the data lake.
• Validating data transformations and ETL (Extract, Transform, Load) processes to ensure data is correctly processed and stored.
• Performing data quality checks to identify and resolve data anomalies and inconsistencies.

Test Planning and Design:

• Developing comprehensive test plans and test cases based on data requirements, use cases, and business rules.
• Designing automated tests to validate data ingestion, transformation, storage, and retrieval processes.
• Collaborating with data engineers and data architects to understand data pipelines and data flow.

Automation and Scripting:

• Writing and maintaining scripts and automated tests to perform data validation and testing.
• Using testing frameworks and tools to automate data testing processes, such as Apache Nifi, Talend, or custom scripts in Python/Scala.

Performance Testing:

• Conducting performance testing to ensure the data lake can handle large volumes of data and high transaction rates.
• Testing the scalability and performance of data ingestion, querying, and processing.

Security and Compliance:

• Ensuring data security and privacy by validating access controls, encryption, and data masking.
• Verifying compliance with relevant data protection regulations and standards.

Integration Testing:

• Testing the integration of the data lake with upstream and downstream systems, such as data sources, data warehouses, and BI tools.
• Ensuring seamless data flow and integration between different components of the data architecture.

Defect Identification and Resolution:

• Identifying defects and issues in the data lake and working with development teams to resolve them.
• Documenting defects, performing root cause analysis, and tracking them to closure.

Documentation and Reporting:

• Creating and maintaining detailed test documentation, including test plans, test cases, test scripts, and test results.
• Reporting test progress, results, and quality metrics to stakeholders.

Continuous Improvement:

• Continuously improving testing processes and methodologies to enhance the efficiency and effectiveness of data testing.
• Staying updated with the latest tools, technologies, and best practices in data testing and data management.