We are seeking a QA Engineer with a strong passion for data quality, performance, and scale to join our Data Platform team.
This role is ideal for a QA professional who enjoys working close to complex data systems, understands large-scale pipelines, and wants to play a key role in shaping the automation and quality strategy of a data engineering organization.
You will act as the primary quality owner for high-volume, mission-critical data platforms, working closely with data engineers, backend developers, and platform teams.
What Youll Do:
Data Quality & Validation:
Design and execute data validation strategies for large-scale batch and streaming pipelines
Ensure data correctness, completeness, freshness, and consistency across the data lake
Define and automate checks for schema changes, data drift, and data quality regressions
Performance & Scalability Testing:
Plan and execute performance and scalability tests for data pipelines and processing jobs
Identify bottlenecks across ingestion, transformation, and querying layers
Partner with engineers to validate performance improvements and prevent regressions
Automation & Infrastructure:
Develop and maintain the data teams QA automation infrastructure
Build reusable testing frameworks and tools tailored for large datasets and pipelines
Integrate automated tests into CI/CD pipelines and production monitoring workflows
Collaboration & Ownership:
Work closely with data engineers, backend developers, and platform engineers throughout the development lifecycle
Act as the sole QA owner within a cross-functional team, driving quality without becoming a bottleneck
Participate in design discussions to ensure testability and observability are built in from the start
Quality Mindset & Communication:
Champion a quality-first culture within the team
Clearly communicate risks, findings, and quality metrics to technical stakeholders
Balance thoroughness with pragmatism in fast-moving, high-scale environments.
This role is ideal for a QA professional who enjoys working close to complex data systems, understands large-scale pipelines, and wants to play a key role in shaping the automation and quality strategy of a data engineering organization.
You will act as the primary quality owner for high-volume, mission-critical data platforms, working closely with data engineers, backend developers, and platform teams.
What Youll Do:
Data Quality & Validation:
Design and execute data validation strategies for large-scale batch and streaming pipelines
Ensure data correctness, completeness, freshness, and consistency across the data lake
Define and automate checks for schema changes, data drift, and data quality regressions
Performance & Scalability Testing:
Plan and execute performance and scalability tests for data pipelines and processing jobs
Identify bottlenecks across ingestion, transformation, and querying layers
Partner with engineers to validate performance improvements and prevent regressions
Automation & Infrastructure:
Develop and maintain the data teams QA automation infrastructure
Build reusable testing frameworks and tools tailored for large datasets and pipelines
Integrate automated tests into CI/CD pipelines and production monitoring workflows
Collaboration & Ownership:
Work closely with data engineers, backend developers, and platform engineers throughout the development lifecycle
Act as the sole QA owner within a cross-functional team, driving quality without becoming a bottleneck
Participate in design discussions to ensure testability and observability are built in from the start
Quality Mindset & Communication:
Champion a quality-first culture within the team
Clearly communicate risks, findings, and quality metrics to technical stakeholders
Balance thoroughness with pragmatism in fast-moving, high-scale environments.
Requirements:
Experience:
Proven experience as a QA Engineer, ideally within data-intensive or platform teams
Hands-on experience testing large-scale systems, pipelines, or distributed architectures
Experience working as the sole QA in a cross-functional engineering team.
Technical Skills:
Strong understanding of data pipelines and data lake concepts
Experience validating large datasets and implementing data quality checks
Familiarity with performance and load testing methodologies
Experience building test automation frameworks (Python preferred)
Understanding of CI/CD pipelines and automation best practices.
Mindset & Collaboration:
Passion for data, performance, and technology
Self-driven, independent, and comfortable owning QA end-to-end
Strong communication skills and ability to collaborate across disciplines
Curious, proactive, and eager to learn complex systems.
Nice to Have:
Experience testing big data or analytics platforms
Familiarity with cloud environments (AWS preferred)
Knowledge of Spark, SQL-based analytics, or data processing frameworks
Experience with data observability or data quality tools.
Experience:
Proven experience as a QA Engineer, ideally within data-intensive or platform teams
Hands-on experience testing large-scale systems, pipelines, or distributed architectures
Experience working as the sole QA in a cross-functional engineering team.
Technical Skills:
Strong understanding of data pipelines and data lake concepts
Experience validating large datasets and implementing data quality checks
Familiarity with performance and load testing methodologies
Experience building test automation frameworks (Python preferred)
Understanding of CI/CD pipelines and automation best practices.
Mindset & Collaboration:
Passion for data, performance, and technology
Self-driven, independent, and comfortable owning QA end-to-end
Strong communication skills and ability to collaborate across disciplines
Curious, proactive, and eager to learn complex systems.
Nice to Have:
Experience testing big data or analytics platforms
Familiarity with cloud environments (AWS preferred)
Knowledge of Spark, SQL-based analytics, or data processing frameworks
Experience with data observability or data quality tools.
This position is open to all candidates.
