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Remote role plus mid-level experience but specialized data-quality focus creates moderate applicant competition.
Data engineering and quality skills are transferable but require domain-specific governance and tooling experience.
Explicit 4–8 years requirement plus specific data-quality and tooling skills increases screening rigor.
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Build and operate automated data quality checks, monitoring, and dashboards integrated into production data pipelines and governance workflows.
Translate governance rules and quality thresholds into executable tests providing near-real-time visibility into data health.
Develop tooling to support auditors, analysts, and stewards, enabling proactive detection and resolution of data quality issues.
4–8 years experience in data engineering, data quality engineering, or related field.
Strong programming skills in Python and SQL; experience building data pipelines and automation.
Experience implementing automated data quality checks, testing, or monitoring frameworks.
Ability to work with cloud data platforms, APIs, and translate written data governance rules into executable logic.
Experienced in embedding data quality into production data workflows rather than post-processing checks.
Skilled at operationalizing data quality dimensions and instrumental in continuous quality improvements.
Comfortable collaborating across governance, data operations, and platform teams to close gaps between rule definition and enforcement.