ETLDatabricksTesting
About the role
JD:
Job Title: ETL Databricks Testing
Experience: 6 Years
Location of Requirement: Chennai / Hyderabad
Required Technical Skill Set:
Strong experience in ETL testing and data warehouse concepts.
Hands-on expertise in Databricks (including notebooks, clusters, and jobs).
Proficiency in SQL for data validation and analysis.
Experience with big data technologies (Spark, PySpark, Delta Lake).
Familiarity with cloud platforms (Azure, AWS, or GCP) and their data services.
Knowledge of data modelling, data quality frameworks, and BI tools.
Strong analytical and problem-solving skills.
Excellent communication and documentation abilities.
Responsibility of / Expectations from the Role:
1 Validate end to end ETL/data pipeline workflows from source systems to target data warehouse layers
2 Perform data reconciliation, data quality checks, and transformation validation across Bronze / Silver / Gold layers (or equivalent)
3 Execute Databricks-based data validations using SQL, notebooks, and result comparisons
4 Design and execute complex SQL queries for data profiling, completeness, accuracy, and referential integrity checks
5 Validate Delta tables, fact/dimension tables, aggregates, and historical load logic
6 Perform job failure analysis, data discrepancy RCA, and support defect triage
7 Collaborate closely with Data Engineering, Analytics, and QA teams during releases
8 . Ensure test coverage for incremental loads, CDC, reprocessing, and batch schedules.
Must-Have:
ETL / Data Warehouse Testing:
Strong experience in DWH concepts (Facts, Dimensions, Star/Snowflake schema)
Hands on data validation across large datasets
Databricks:
Experience validating data pipelines built on Databricks
Working knowledge of notebooks and Delta Lake concepts
SQL:
Strong proficiency in writing complex SQL queries (joins, subqueries, aggregations, window functions)
Ability to perform large volume data validation and reconciliation
Python (Basic to Intermediate):
Ability to read/write simple Python scripts for data validation, automation support, or log analysis.
Good-to-Have:
HADOOP HIVE, Teradata, SQL application experience
Understanding of CI/CD data pipelines and release validation
Experience in Healthcare / Insurance / Enterprise analytics domains
Automation knowledge for data validation (any framework).
What you'll do
JD:
Job Title: ETL Databricks Testing
Experience: 6 Years
Location of Requirement: Chennai / Hyderabad
Required Technical Skill Set:
Strong experience in ETL testing and data warehouse concepts.
Hands-on expertise in Databricks (including notebooks, clusters, and jobs).
Proficiency in SQL for data validation and analysis.
Experience with big data technologies (Spark, PySpark, Delta Lake).
Familiarity with cloud platforms (Azure, AWS, or GCP) and their data services.
Knowledge of data modelling, data quality frameworks, and BI tools.
Strong analytical and problem-solving skills.
Excellent communication and documentation abilities.
Responsibility of / Expectations from the Role:
1 Validate end to end ETL/data pipeline workflows from source systems to target data warehouse layers
2 Perform data reconciliation, data quality checks, and transformation validation across Bronze / Silver / Gold layers (or equivalent)
3 Execute Databricks-based data validations using SQL, notebooks, and result comparisons
4 Design and execute complex SQL queries for data profiling, completeness, accuracy, and referential integrity checks
5 Validate Delta tables, fact/dimension tables, aggregates, and historical load logic
6 Perform job failure analysis, data discrepancy RCA, and support defect triage
7 Collaborate closely with Data Engineering, Analytics, and QA teams during releases
8 . Ensure test coverage for incremental loads, CDC, reprocessing, and batch schedules.
Must-Have:
ETL / Data Warehouse Testing:
Strong experience in DWH concepts (Facts, Dimensions, Star/Snowflake schema)
Hands on data validation across large datasets
Databricks:
Experience validating data pipelines built on Databricks
Working knowledge of notebooks and Delta Lake concepts
SQL:
Strong proficiency in writing complex SQL queries (joins, subqueries, aggregations, window functions)
Ability to perform large volume data validation and reconciliation
Python (Basic to Intermediate):
Ability to read/write simple Python scripts for data validation, automation support, or log analysis.
Good-to-Have:
HADOOP HIVE, Teradata, SQL application experience
Understanding of CI/CD data pipelines and release validation
Experience in Healthcare / Insurance / Enterprise analytics domains
Automation knowledge for data validation (any framework).
What we're looking for
JD:
Job Title: ETL Databricks Testing
Experience: 6 Years
Location of Requirement: Chennai / Hyderabad
Required Technical Skill Set:
Strong experience in ETL testing and data warehouse concepts.
Hands-on expertise in Databricks (including notebooks, clusters, and jobs).
Proficiency in SQL for data validation and analysis.
Experience with big data technologies (Spark, PySpark, Delta Lake).
Familiarity with cloud platforms (Azure, AWS, or GCP) and their data services.
Knowledge of data modelling, data quality frameworks, and BI tools.
Strong analytical and problem-solving skills.
Excellent communication and documentation abilities.
Responsibility of / Expectations from the Role:
1 Validate end to end ETL/data pipeline workflows from source systems to target data warehouse layers
2 Perform data reconciliation, data quality checks, and transformation validation across Bronze / Silver / Gold layers (or equivalent)
3 Execute Databricks-based data validations using SQL, notebooks, and result comparisons
4 Design and execute complex SQL queries for data profiling, completeness, accuracy, and referential integrity checks
5 Validate Delta tables, fact/dimension tables, aggregates, and historical load logic
6 Perform job failure analysis, data discrepancy RCA, and support defect triage
7 Collaborate closely with Data Engineering, Analytics, and QA teams during releases
8 . Ensure test coverage for incremental loads, CDC, reprocessing, and batch schedules.
Must-Have:
ETL / Data Warehouse Testing:
Strong experience in DWH concepts (Facts, Dimensions, Star/Snowflake schema)
Hands on data validation across large datasets
Databricks:
Experience validating data pipelines built on Databricks
Working knowledge of notebooks and Delta Lake concepts
SQL:
Strong proficiency in writing complex SQL queries (joins, subqueries, aggregations, window functions)
Ability to perform large volume data validation and reconciliation
Python (Basic to Intermediate):
Ability to read/write simple Python scripts for data validation, automation support, or log analysis.
Good-to-Have:
HADOOP HIVE, Teradata, SQL application experience
Understanding of CI/CD data pipelines and release validation
Experience in Healthcare / Insurance / Enterprise analytics domains
Automation knowledge for data validation (any framework).