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Remote mid-level data role with common skills increases applicant density despite niche data-quality requirement.
Data-engineering skills are transferable, but governance and tooling needs moderately limit cross-industry fit.
Explicit 4–8 year requirement plus mandatory coding and data-quality tooling raises screening strictness.
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Build and operate automated data quality tooling including checks, monitoring, alerting, and dashboards integrated into data pipelines and governance workflows.
Maintain reliable and performant data quality infrastructure to provide near-real-time visibility into data health and reduce quality issues downstream.
Collaborate with Governance, Data Operations, and platform teams to translate rules into executable tests and embed quality into data movement processes.
4–8 years of experience in data engineering, data quality engineering, analytics engineering, or related fields.
Strong programming skills in Python and SQL with experience building data pipelines or automation.
Experience implementing automated data quality checks, testing, or monitoring frameworks.
Familiarity with cloud data platforms, APIs, and data governance/quality tooling.
Experienced in operationalizing data quality dimensions and translating written rules into executable logic in production environments.
Skilled in developing tooling that supports auditors and analysts, enabling measurable improvements in data quality processes.
Comfortable working in cross-functional teams embedding data quality into data pipelines and governance processes.