Senior QA Engineer – Data & Analytics
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Job Description
Senior QA Engineer ensuring data accuracy across pipelines, migrations and analytics platforms. Supporting quality automation, observability and governance for business-critical data environments.
Responsibilities:
- Define and implement data quality and validation strategies across data pipelines, data lakes and warehouses
- Test data throughout the full lifecycle for accuracy, completeness, consistency and integrity
- Validate ETL/ELT pipelines and data transformations from source systems through downstream analytics platforms
- Develop and execute end-to-end data validation and reconciliation tests
- Use SQL for data validation, reconciliation and investigation
- Implement data contracts, schema validation and anomaly detection with engineering teams
- Support data quality automation using frameworks such as Great Expectations
- Perform exploratory data testing and investigate discrepancies across systems and environments
- Validate BI and analytics outputs against underlying data
- Support large-scale data migration testing and data integrity during cloud transitions
- Define and monitor validation criteria, data freshness SLAs and quality metrics
- Contribute to data observability, lineage, governance and monitoring strategies
- Identify recurring data quality issues and improve processes with engineering teams
- Support performance testing for query performance, scalability and reliability across data platforms
- Contribute to data security, privacy and compliance testing where required
- Work as part of an agile software development team with Data Engineers, Analysts, Software Engineers and other stakeholders
Requirements:
- Strong professional experience in QA within data-focused projects
- Hands-on experience testing data pipelines, ETL/ELT processes and data transformations
- Strong SQL skills for data validation, reconciliation and troubleshooting
- Understanding of data engineering workflows and data lake/warehouse architectures
- Experience testing APIs, integrations and data flows in cloud environments such as GCP, AWS or Azure
- Experience with data quality frameworks such as Great Expectations or similar
- Experience validating data across multiple systems and identifying discrepancy root causes
- Understanding of validating BI and analytics outputs using ThoughtSpot, Power BI, Looker or Tableau
- Strong analytical and problem-solving skills with excellent attention to detail
- Excellent communication and collaboration skills with Data Engineers, Analysts, Developers and stakeholders
- Proactive quality mindset and willingness to advocate for data quality throughout the development lifecycle
- Experience working in an Agile, cross-functional team
- Strong verbal and written communication skills in English
- Nice to have: experience testing large-scale cloud or data platform migrations, particularly Databricks-to-GCP environments
- Nice to have: experience with Databricks and/or GCP
- Nice to have: understanding of data observability, lineage tracking and monitoring
- Nice to have: performance and scalability testing for large data platforms
- Nice to have: knowledge of data governance, security and privacy best practices
- Nice to have: exposure to machine learning model validation or AI testing
- Nice to have: experience with large-scale or business-critical data environments
Benefits:
- Collegial environment with shared responsibility and authority
- Culture focused on learning from mistakes rather than criticising failure
- Agile environment where ideas are welcome
- Possibility to grow and experience different projects
- Ongoing training and mentoring
- Possibility of travelling
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