Data Warehouse Testing

Relevant Experience

5-8 / 8-10 / 10+

Detailed JD

Must have skills - 2 skills which are non-negotiable

ETL, SQL, Python, Spark

Desirable skills - 1 skill which is nice to have

Big Data Tech Stacks

Desired experience range

5-8 years

Responsibilities and JD in brief along with additional criteria to be considered (if any):

Hands-on experience in ETL Testing, Data Warehouse Testing, and Data Migration Testing.

Solid expertise in SparkSQL and database validation across Oracle, SQL Server, PostgreSQL, Snowflake, AWS Redshift, or BigQuery.

Validate source-to-target mappings, transformation rules, data lineage, and business logic as per mapping documents.

Experience in Python-based ETL test automation.

Perform data reconciliation, completeness checks, referential integrity, duplicate detection, and data quality validations.

Experience in ETL tools such as Informatica, Ab Initio, ODI, SSIS, DataStage, Azure Data Factory, or Databricks.

Strong understanding of Data Warehousing concepts including Facts, Dimensions, Star Schema, and SCD validations.

Experience designing and implementing Spark-based Data Validation frameworks to validate large-scale financial data across batch and ETL pipelines, ensuring data quality, completeness, accuracy, and consistency throughout the project lifecycle.

Experience developing automated data quality controls and validation test suites using Apache Spark, including reconciliation, schema validation, data profiling, and exception handling for high-volume financial datasets

Experience validating cloud-based data platforms on AWS, Azure, Databricks, Snowflake, or GCP.

Hands-on experience with batch job validation, workflow monitoring, and scheduling tools such as Autosys, Control-M, or Airflow.

Develop automation solutions using Python/Shell scripting for ETL and data validation activities.

Implement test strategies, prepare and execute test cases, analyze results, and coordinate defect resolution to ensure data quality.

Collaborate with business, development, and data engineering teams to ensure accurate and reliable data delivery.

Maintain detailed documentation of test scenarios, SQL validations, automation assets, and testing processes.

Nice to Have Skills:

Experience in BI Reporting validation using Power BI, Tableau, Qlik, or MicroStrategy.

Experience in Big Data technologies such as Hadoop, Spark, Hive, and Kafka.

Knowledge of AI-enabled Data Quality and Data Governance solutions.

Banking, Capital Markets, Wealth Management, or Mortgage domain experience.

Your Contribution to the Team





Strong analytical and problem-solving skills.

Ability to understand complex business transformations and convert them into comprehensive test scenarios.

Passion for ensuring data accuracy, integrity, and compliance.

Proactive approach to identifying data defects and root cause analysis.

Ability to collaborate effectively with Data Engineers, Architects, Business Analysts, and Product Owners.

Required/Basic Qualifications

Bachelor's degree or foreign equivalent required from an accredited institution.

At least 4 years of experience in Data/ETL Testing.

Strong experience in SQL, Data Validation, Data Reconciliation, Python.

Experience working in Agile delivery environments.

JD

Proven experience working as a QA Tester in ETL environments.

Strong proficiency in SQL, ETL testing.

Strong proficiency in Python automation testing.

Familiarity with Agile methodologies and experience working in Agile teams.

Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams..

Mandatory skills

Must have skills - 2 skills which are non-negotiable

ETL, SQL, Python, Spark

Desirable skills - 1 skill which is nice to have

Big Data Tech Stacks

Desired experience range

4-8 years

Responsibilities and JD in brief along with additional criteria to be considered (if any):

Hands-on experience in ETL Testing, Data Warehouse Testing, and Data Migration Testing.

Solid expertise in SparkSQL and database validation across Oracle, SQL Server, PostgreSQL, Snowflake, AWS Redshift, or BigQuery.

Validate source-to-target mappings, transformation rules, data lineage, and business logic as per mapping documents.

Experience in Python-based ETL test automation.

Perform data reconciliation, completeness checks, referential integrity, duplicate detection, and data quality validations.

Experience in ETL tools such as Informatica, Ab Initio, ODI, SSIS, DataStage, Azure Data Factory, or Databricks.

Strong understanding of Data Warehousing concepts including Facts, Dimensions, Star Schema, and SCD validations.





Experience designing and implementing Spark-based Data Validation frameworks to validate large-scale financial data across batch and ETL pipelines, ensuring data quality, completeness, accuracy, and consistency throughout the project lifecycle.

Experience developing automated data quality controls and validation test suites using Apache Spark, including reconciliation, schema validation, data profiling, and exception handling for high-volume financial datasets

Experience validating cloud-based data platforms on AWS, Azure, Databricks, Snowflake, or GCP.

Hands-on experience with batch job validation, workflow monitoring, and scheduling tools such as Autosys, Control-M, or Airflow.

Develop automation solutions using Python/Shell scripting for ETL and data validation activities.

Implement test strategies, prepare and execute test cases, analyze results, and coordinate defect resolution to ensure data quality.

Collaborate with business, development, and data engineering teams to ensure accurate and reliable data delivery.

Maintain detailed documentation of test scenarios, SQL validations, automation assets, and testing processes.

Nice to Have Skills:

Experience in BI Reporting validation using Power BI, Tableau, Qlik, or MicroStrategy.

Experience in Big Data technologies such as Hadoop, Spark, Hive, and Kafka.

Knowledge of AI-enabled Data Quality and Data Governance solutions.

Banking, Capital Markets, Wealth Management, or Mortgage domain experience.

Your Contribution to the Team

Strong analytical and problem-solving skills.

Ability to understand complex business transformations and convert them into comprehensive test scenarios.

Passion for ensuring data accuracy, integrity, and compliance.

Proactive approach to identifying data defects and root cause analysis.

Ability to collaborate effectively with Data Engineers, Architects, Business Analysts, and Product Owners.

Required/Basic Qualifications

Bachelor's degree or foreign equivalent required from an accredited institution.

At least 4 years of experience in Data/ETL Testing.

Strong experience in SQL, Data Validation, Data Reconciliation, Python.

Experience working in Agile delivery environments.

JD

Proven experience working as a QA Tester in ETL environments.

Strong proficiency in SQL, ETL testing.

Strong proficiency in Python automation testing.

Familiarity with Agile methodologies and experience working in Agile teams.

Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.

Pay: ₹600,000.00 - ₹1,500,000.00 per year

Work Location: In person

📌 Data Warehouse Testing (India)
🏢 Dananda Talents
📍 India