OP
Quality Assurance Automation Engineer at Optix enhancing automated QA practices for flex and coworking spaces. Collaborating with engineering teams to ensure stability and reliability of services.
About the role
- Definition der Teststrategie über alle Ebenen (Unit/Data Unit, Integration, System, UAT), inkl. CI/CD-Integration
- Planung, Konzeption und Durchführung von End-to-End- und Integrationstests sowie UAT-Enablement
- Aufbau/Weiterentwicklung automatisierter Test-Suites für ETL/ELT-Pipelines, Data Lineage, Schemas, DQ-Regeln, SLAs
- Konzeption von synthetischen/anonymisierten Testdaten und Verwaltung von Testumgebungen zur Sicherstellung der Reproduzierbarkeit
- Einrichtung/Betreuung von Testplänen, Traceability (Requirements → Tests → Defects) in ALM/Quality Center und Jira
- Testen von Berechtigungen/Row-Level-Security/Policies, Unity Catalog-Konformität und Data Privacy
- Definition von QA-KPIs (Defect Leakage, Test Coverage, DQ-Scores, Pipeline Reliability) und Management-Reporting
- Etablierung von Best Practices, Schulung von Engineers/Analysten und Review von PRs/MRs mit Test-Fokus
Requirements
- 5+ Jahre Erfahrung als (Senior) Test Engineer/QA im DWH/Data-Plattform-Umfeld (Integrationstests, End-to-End, UAT)
- Sehr gute Deutsch- und Englischkenntnisse
- ALM/Quality Center und Jira – gelebte Traceability, sauberer Defect-Lifecycle
- Testautomatisierung im Data-Kontext (z. B. Validierung von ETL/ELT, Schemas, Datenqualitätsregeln, KPIs/SLAs)
- Starkes konzeptionelles Know-how in Testdesign, risiko-basiertem Testen, Abdeckung/Nutzwert von Tests
- Solides Verständnis moderner Datenarchitekturen (DWH, Data Lake/Lakehouse, Batch/Streaming)
- Analytische, strukturierte Arbeitsweise
- Starke Kommunikationsfähigkeit auf Deutsch – Brücke zwischen Business, Data Engineering und Architektur
- Pragmatismus: Qualität priorisieren, ohne Geschwindigkeit zu verlieren
- Nice-to-have: Python (Databricks Notebooks, PySpark, Delta Live Tables)
- Nice-to-have: Databricks Unity Catalog (Berechtigungs-/Policy-Tests, Metadaten- und Lineage-Validierung)
- Nice-to-have: Erfahrung mit AI-basierter Testgenerierung, Synthetic Data, Anomalie-Detection
- Nice-to-have: Erfahrung mit Data Quality-Frameworks
- Nice-to-have: Kenntnisse in CI/CD und IaC
- Nice-to-have: Non-Functional-Testing im Data-Kontext (Performance, Skalierung, Kosten/FinOps, Resilienz)
Benefits
- Pensum: 80% - 100%
- Remote/Home Office: Möglich nach Absprache
- Dauer: Bis Ende 2026 mit Möglichkeit auf Verlängerung
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
BG
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