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Lead Software Engineer
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Job Description
Job Description: Data QA Engineer
Overview
The Data QA Engineer is responsible for validating data accuracy, ETL processes,
reporting outputs, and data quality across the organization. This role ensures business-
critical data is reliable, consistent, and meets defined quality standards. The candidate
will work in Agile teams and contribute to improving data testing practices, including
leveraging automation tools such as DataGaps.
Responsibilities
Perform end-to-end testing for data pipelines, ETL/ELT processes, data
warehouses, and data lakes.
Validate data transformations, aggregation logic, schema changes, and mapping
rules.
Conduct data profiling, anomaly detection, and root-cause analysis for data
issues.
Write complex SQL queries for data validation and reconciliation.
Collaborate with Data Engineering, Analytics, BI, and Product teams within an
Agile/Scrum environment.
Review data models, data lineage, functional requirements, and business rules.
Log, track, and manage defects using Jira, ensuring proper documentation and
timely resolution.
Participate in sprint ceremonies including planning, grooming, and retrospectives.
Support continuous improvements in data testing processes, standards, and
documentation.
Use Datagaps (BI Validator / ETL Validator) for data validation automation, report
testing, and source-to-target comparison.
Required Skills
Strong SQL skills (window functions, joins, aggregates, complex queries).
Solid understanding of ETL/ELT processes and data warehousing concepts.
Experience testing dashboards and reports on BI platforms (Tableau, Power BI,
Looker).
Familiarity with Agile methodologies (Scrum/Kanban).
Hands-on experience using Jira for defect creation, tracking, and reporting.
Strong analytical mindset and high attention to detail.
Preferred Skills
Experience with Datagaps tools (BI Validator, ETL Validator) or any other tools for
automated data testing.
Exposure to other data validation tools (QuerySurge, IceDQ, Wiiisdom Ops,
Great Expectations).
Knowledge of Python or other scripting languages.
Experience with cloud platforms (AWS, Azure, GCP).
Understanding of data governance and metadata management
Overview
The Data QA Engineer is responsible for validating data accuracy, ETL processes,
reporting outputs, and data quality across the organization. This role ensures business-
critical data is reliable, consistent, and meets defined quality standards. The candidate
will work in Agile teams and contribute to improving data testing practices, including
leveraging automation tools such as DataGaps.
Responsibilities
Perform end-to-end testing for data pipelines, ETL/ELT processes, data
warehouses, and data lakes.
Validate data transformations, aggregation logic, schema changes, and mapping
rules.
Conduct data profiling, anomaly detection, and root-cause analysis for data
issues.
Write complex SQL queries for data validation and reconciliation.
Collaborate with Data Engineering, Analytics, BI, and Product teams within an
Agile/Scrum environment.
Review data models, data lineage, functional requirements, and business rules.
Log, track, and manage defects using Jira, ensuring proper documentation and
timely resolution.
Participate in sprint ceremonies including planning, grooming, and retrospectives.
Support continuous improvements in data testing processes, standards, and
documentation.
Use Datagaps (BI Validator / ETL Validator) for data validation automation, report
testing, and source-to-target comparison.
Required Skills
Strong SQL skills (window functions, joins, aggregates, complex queries).
Solid understanding of ETL/ELT processes and data warehousing concepts.
Experience testing dashboards and reports on BI platforms (Tableau, Power BI,
Looker).
Familiarity with Agile methodologies (Scrum/Kanban).
Hands-on experience using Jira for defect creation, tracking, and reporting.
Strong analytical mindset and high attention to detail.
Preferred Skills
Experience with Datagaps tools (BI Validator, ETL Validator) or any other tools for
automated data testing.
Exposure to other data validation tools (QuerySurge, IceDQ, Wiiisdom Ops,
Great Expectations).
Knowledge of Python or other scripting languages.
Experience with cloud platforms (AWS, Azure, GCP).
Understanding of data governance and metadata management
More Info
Job Type:
Industry:
Employment Type:
Key Skills
ETL Validator)
QuerySurge
Great Expectations
IceDQ
Datagaps tools (BI Validator
ELT processes
GCP)
ETL processes
Cloud platforms (AWS
Wiiisdom Ops
About Company
