Data Quality & Benchmarking Engineer

We are looking for a Data Quality & Benchmarking Engineer to strengthen the quality process of our DataPilot product and related data/ML platform components.

This role is embedded within the development team and is responsible for ensuring that features are validated early, tested against realistic datasets, benchmarked properly, and aligned with product requirements and technical designs before they reach final product acceptance.

The mission is not only to find bugs at the end of the development cycle. The mission is to improve how we define, verify, measure, and release quality throughout the development process.
You will help us reduce the gap between product requirements, technical design, and final implementation by introducing structured validation, dataset-based regression testing, benchmarking practices, and exploratory testing for scenarios that cannot be fully automated.

Responsibilities
As a Data Quality & Benchmarking Engineer, you will:

• Review product requirements, technical designs, and acceptance criteria before and during implementation.
• Translate PRDs and technical designs into clear validation scenarios, edge cases, and test plans.
• Verify that implemented features match the agreed product behavior and technical design.
• Define and maintain test datasets for our platforms, including golden datasets, dirty datasets, large datasets, edge-case datasets, and regression datasets.
• Validate data processing behavior, data quality checks, labeling flows, correction flows, exports, and user-facing results.
• Design and execute benchmark scenarios for our timeseries platform features and Kubeflow pipelines.
• Measure runtime, memory usage, throughput, scalability limits, failure behavior, and regression between releases.
• Perform exploratory testing for complex user flows, realistic data scenarios, and cases that are hard or inefficient to automate.
• Identify which validation checks should become automated tests and collaborate with developers to implement them.
• Support release decisions by providing clear quality findings, benchmark results, and risk assessments.
• Work closely with Product, Backend, Frontend, Data, MLOps, and Platform engineers to improve the overall quality process.
• Help define and improve the team’s Definition of Done, engineering acceptance process, and quality gates.
• Document test scenarios, dataset assumptions, benchmark results, and known limitations.

 
What success looks like
Success in this role means that:

• Product receives features that have already been validated against the PRD and design.
• Fewer issues are discovered late during product acceptance.
• DataPilot has a structured catalog of test datasets and regression scenarios.
• Benchmark results are available for important features and pipeline changes.
• Performance or data-quality regressions are detected before release.
• Developers receive earlier feedback during implementation.
• Quality becomes a shared engineering practice, not a final handover step.

 
What this role is not

• This is not a traditional end-stage QA role where the main responsibility is to test finished features after development is complete.
• This role is also not limited to clicking through UI flows or executing predefined test cases.
• The role is part of the engineering process and focuses on early validation, data-quality verification, regression testing, benchmarking, and release confidence.

We are looking for someone with a few years of hands-on experience in software quality, test engineering, data validation, or data platform testing. Someone who thinks like an engineer, not only as a tester.
The ideal candidate has experience beyond manual testing and is comfortable working closely with engineering teams, reading technical designs, understanding data flows, and creating practical validation strategies.
You should have:

• 2+ years of experience in quality engineering, test engineering, data engineering testing, SDET, or a similar role.
• Experience testing complex software systems, preferably involving data processing, backend services, APIs, pipelines, or platform workflows.
• Strong ability to understand product requirements and convert them into concrete test scenarios.
• Experience with test design, exploratory testing, regression testing, and acceptance criteria validation.
• Practical experience with Python-based testing or scripting.
• Experience working with datasets, structured data, logs, outputs, or data-quality validation.
• Familiarity with CI/CD workflows and modern development processes.
• Ability to communicate findings clearly to developers, product managers, and technical leads.
• A structured mindset and the ability to define repeatable quality processes from scratch.

Technical skills
Relevant experience may include:

• Python and pytest or similar testing frameworks
• REST API testing
• Basic understanding of CI/CD pipelines
• Basic understanding of docker or containerized environments
• Observability tools, logs, metrics, or dashboards

The following are bonus:

• Basic understanding of Data/ML Pipelines
• Performance benchmarking
• time-series data processing