DWH testing Professional

As a QA Automation Engineer, you will be an integral part of the data engineering team, ensuring the quality and reliability of the lakehouse infrastructure and data pipelines. You will design, develop, and implement automated testing solutions to validate data ingestion, transformation, and processing workflows. By collaborating closely with data engineers and stakeholders, you will play a critical role in delivering high-quality data solutions that meet business needs.

Key Responsibilities:
Test Strategy and Planning:
Develop and execute comprehensive test plans and strategies for data engineering pipelines and processes, focusing on functional, integration, performance, and scalability testing.
Automation Development:
Build and maintain automation frameworks and test scripts using Python, PySpark, or other scripting languages to validate ETL pipelines and lakehouse infrastructure.
Automate data validation, schema verification, and query performance testing for Azure Databricks Delta Tables, Delta Lake, and other data sources.
Data Quality and Validation:
Validate data transformations, models, and storage to ensure accuracy, consistency, and compliance with business requirements.
Implement automated checks for data freshness, completeness, and integrity in batch and streaming data pipelines.
Collaboration:
Work closely with data engineers, product owners, and other stakeholders to understand data requirements and translate them into test cases.
Partner with DevOps teams to integrate testing into CI/CD pipelines using Azure DevOps and GitHub Actions.
Monitoring and Reporting:
Monitor test execution and analyze results to identify defects, performance bottlenecks, or scalability issues.
Document and report defects, root causes, and recommendations for resolution.

Skills You MUST Have:
Data Engineering Testing:
5+ years of experience in QA automation for data engineering, ETL, or similar domains.
Experience with data testing tools and frameworks such as Great Expectations or similar.
Strong expertise in Python for test automation, with experience in libraries like PyTest or unittest.
Proficiency in SQL for data validation and querying.
Hands-on experience with testing large-scale data pipelines in data platforms like Azure Databricks (preferred) or Snowflake
Azure Ecosystem Expertise:
Experience with Azure Data Cloud - ADF, Synapse, ADLS Gen 2, CosmosDB
Familiarity with Azure Event Hub or Kafka for validating streaming data pipelines.
Understanding of Azure Blob Storage and Azure Data Lake for data validation tasks.
Exposure to monitoring tools like Azure Monitor and Azure Log Analytics for analyzing test results and pipeline performance.
Familiarity with data security and governance practices for Azure Databricks
Familiarity with modern data lakehouse data formats - delta table, parquet
Knowledge of data modeling concepts such as Star Schema, Data Vault and Medallion Architecture
DevOps:
Experience with CI/CD pipelines and build automation using GitHub Actions.
Familiarity with Agile development methodologies.

Mandatory skills
Azure Databricks
Pyspark
Python
DWH Testing