Quality Engineer - Analytics

What You'll Do

Build & Evolve Data Quality Frameworks

• Design, implement, and continuously improve data quality frameworks across the data platform.
• Build and maintain automated data quality tests, validation controls, monitoring frameworks, and source freshness checks throughout the data lifecycle.
• Establish quality standards and practices that can be consistently adopted across the organisation.
• Embed data quality controls throughout the data development lifecycle to improve reliability before issues reach production.

Develop Intelligent Testing, Monitoring & Anomaly Detection

• Develop statistical checks and anomaly detection techniques to identify unexpected changes in data and business metrics.
• Design intelligent testing approaches that balance rigorous quality standards with pragmatic operational practices.
• Identify and distinguish between minor data inconsistencies and data quality incidents requiring immediate action.
• Continuously improve monitoring approaches to maximise data reliability while minimising unnecessary alerts and alert fatigue.

Design Alerting & Incident Management Processes

• Design alerting and notification frameworks that prioritise data quality issues based on business impact.
• Establish incident management processes for responding to critical data quality issues.
• Investigate data quality incidents and perform end-to-end root cause analysis across ingestion, transformation, and reporting layers.
• Translate technical data quality issues into clear business impact and actionable improvements.

Partner Across the Data & Analytics Organisation

• 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.

Enable Data Quality Visibility & Continuous Improvement

• 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.

What It Takes

Technical

• 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.

Leadership & Collaboration

• 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.

Mindset

• 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.