Quality Engineer - Analytics

Work closely with Analytics Engineers, Data Engineers, Data Analysts, and other stakeholders to improve the quality and reliability of data products.

Collaborate with cross-functional teams to understand business processes, KPIs, and the impact of data quality issues on business outcomes.

Support teams in identifying quality risks across complex data pipelines and systems.

Drive consistent adoption of data quality standards, automation, testing, and continuous validation practices.

Create and maintain reporting and dashboards that provide visibility into data quality health across the platform.

Track and communicate test coverage, data quality incidents, alert volumes, and quality trends.

Use data quality insights to identify recurring issues, improvement opportunities, and areas of operational risk.

Guide and train teams on data quality frameworks, testing strategies, observability practices, and reliability standards.

4+ years of experience in Analytics Engineering, Data Analytics, Data Quality, or a related data-focused role.

Strong SQL skills and experience working with analytical datasets and data models.

Strong analytical and statistical mindset, with the ability to identify patterns, anomalies, and data quality risks.

Strong understanding of business processes, KPIs, and the impact of data quality issues on business outcomes.

Experience designing and implementing data quality controls, monitoring frameworks, validation processes, and source freshness checks.

Experience embedding automated testing, validation, and quality controls into CI/CD and development workflows.

Experience performing root cause analysis across complex data pipelines and systems.

Hands-on experience with dbt, SQLMesh, or similar SQL-based transformation tools.

Experience working with modern data platforms and cloud data warehouses is preferred.

Experience working closely with Analytics Engineers, Data Engineers, Data Analysts, and cross-functional stakeholders.

Strong communication and stakeholder management skills, with the ability to translate technical data quality issues into business impact.

Ability to influence teams and drive consistent adoption of data quality standards and best practices.

Strong problem-solving skills with a structured and pragmatic approach to investigating data quality issues.

Ability to balance technical quality requirements with operational priorities and business impact.

Quality-focused mindset with a strong commitment to data reliability, accuracy, and trust.

Pragmatic approach to problem solving, recognising that not every data issue carries the same level of business impact.

Analytical and curious approach to identifying patterns, anomalies, and underlying causes.

Continuous improvement mindset with a focus on automation, scalability, and operational excellence.

Strong sense of ownership for the reliability and trustworthiness of data products.

Collaborative mindset with a passion for enabling teams to adopt better data quality and reliability practices.