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Generalist mid-level data role in a metro with broad skills (SQL, dbt) drives high candidate competition.
Data-quality engineering skills are moderately transferable, but platform and analytics experience create medium industry specificity.
Explicit 4+ years requirement plus mandatory SQL, dbt/SQLMesh, CI/CD and data-pipeline expertise raises strictness to high.
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Design, implement, and maintain automated data quality frameworks, tests, and monitoring systems across the data platform.
Develop intelligent anomaly detection, alerting, and incident management processes to prioritise and resolve critical data quality issues.
Collaborate with cross-functional data teams to embed data quality controls and drive consistent adoption of standards and continuous improvement.
4+ years of experience in Analytics Engineering, Data Analytics, Data Quality, or related data role.
Strong SQL skills and experience with analytical datasets and data models.
Experience designing and implementing automated data quality controls, validation processes, and monitoring frameworks.
Experience performing root cause analysis across complex data pipelines and systems.
Proven ability to translate technical data quality issues into business impact and influence cross-functional teams to adopt quality standards.
Experience working with SQL-based transformation tools such as dbt or SQLMesh and modern cloud data platforms.
Structured and pragmatic problem-solving approach with a focus on balancing data quality rigor and operational priorities.