V
Lead Software Engineer
V
- Posted 11 hours ago
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Job Description
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.
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
About Company
Skills:
Oracle Pl Sql, PostgreSQL, Modern Tooling, Optimization Tooling, Scripting Systems, Core Database Design
Skills:
Java, Graphql, Spring Boot, API design, Sql, React, REST, Docker, ECS, Databricks, Kubernetes, AWS, LangChain, AI orchestration frameworks, Infrastructure as Code, EKS, Generative AI systems, CI/CD pipelines, LLM evaluation frameworks, LlamaIndex
Skills:
secure coding , Performance Tuning, Spring Boot, Grafana, Angular, Microservices, React, Dynatrace, Splunk, AWS, Test strategy automation, Incident root-cause analysis, CI/CD strategy and execution, Resilient highly available architectures, Observability and monitoring, AI-assisted software development tools, SQL databases, Kafka event-driven architecture