Data Quality Engineer

Data Quality Engineer – Quality Engineering | Hybrid – Austin, TexasYou know quality is more than a checkbox. It’s the foundation of reliable, intelligent systems – and without trusted data, nothing else works.As the client expands their asset intelligence capabilities following a recent acquisition, they’re building scalable pipelines and deploying on-prem network sensors that dramatically improve asset visibility. This is a pivotal moment to join as a Data Quality Engineer, where your work will ensure that the data powering ML and analytics pipelines is consistently accurate, complete, and trusted.Why This Role?You’ll join a high-performing Quality Engineering team focused on embedding testing and validation into every layer of data and ML pipeline development. This is a hands-on, technical role – but one where your impact will be felt across the business, as the reliability of these systems is central to future growth.You’ll lead on test automation across integrated data pipelines, drive CI/CD integration of quality checks, and help the company scale its data reliability as systems become more complex.Where You’ll Make an ImpactBuild automated data quality test frameworks across ML and analytics pipelinesImplement and maintain end-to-end regression and integration tests in CI/CD (CircleCI, GitHub Actions)Deploy and test network sensors across multiple IT environments (TAP, SPAN, NETFLOW, etc.)Validate integration of Redjack’s pipelines with the wider architecture of the acquiring companyDesign monitoring dashboards, anomaly detection pipelines, and alerts for proactive quality managementCollaborate with cross-functional teams to co-design test plans and evolve testing strategiesCreate test coverage across structured and unstructured data in production ML systemsYou’ll Thrive If You Bring4+ years’ experience in Quality Engineering, ML Test Automation or Data QualityProficiency in Python and SQL to build validation tools and test frameworksHands-on experience with CI/CD pipelines and orchestration tools (Airflow, MLflow, Kubeflow, GitHub Actions)Strong understanding of distributed systems (e.g. Kafka, APIs) and cloud infrastructure (AWS S3, Snowflake, BigQuery)Familiarity with data quality and validation frameworks such as Great Expectations, dbt, or DeequBonus Points ForExposure to Rust or networking environmentsExperience with mocking libraries (Mockito, mountebank)Containerisation and orchestration tools (Docker, Kubernetes)Multi-cloud familiarity: AWS, GCP, and AzureWhat’s on OfferCompetitive salary aligned with Austin market benchmarksHealth insurance, paid time off, and hybrid working (must be Austin-based)Career growth within a scaling, global software organisation focused on AI and data intelligenceDedicated budget for continuous learning across AI reliability, data governance, and QE