Data Quality Developer

My responsibilities:

• Implement data quality checks across multiple data domains, including master data, transactional data, data in transit, reconciliation, and analytical/reporting data
• Build and maintain Table Monitors to detect pipeline delays/breakages and data health issues per table/view, including: freshness, volume expectations, and schema changes
• Build and maintain Metric Monitors to identify anomalies in key statistical/business KPIs and to run comparisons/reconciliation across systems (e.g., different instances or data warehouses) while validating agreed tolerances
• Build and maintain Validation Monitors to identify bad individual rows and enforce business logic (e.g., custom SQL validations for complex master data logic; row-level business rules)
• Build and maintain Query Performance Monitors to detect inefficient/problematic queries that increase compute costs or risk timeouts and downstream data quality incidents
• Integrate data quality monitoring into the data pipeline / data product lifecycle (setup, deployment, and ongoing operational maintenance)
• Collaborate with data engineers and analytics teams to investigate data quality incidents, perform root-cause analysis, and implement preventive fixes
• Document implemented checks/monitors, including intended meaning, owners, and guidance on how to interpret alerts

The knowledge I own:

• Good knowledge of data management, data quality, and data architecture
• Practical experience implementing data quality rules/checks and operating quality monitoring solutions for structured data
• Strong skills in SQL and Python
• Understanding of data quality dimensions (completeness, correctness/accuracy, consistency, uniqueness, validity, timeliness) and how to translate them into measurable checks
• Familiarity with data-in-transit monitoring and reconciliation patterns across systems
• Strong collaboration and communication skills to work with multiple stakeholders (data engineering, data product, analytics/reporting)
• Background in data engineering environments (advantage): data lake/warehouse, pipeline orchestration, and CI/CD for data/pipeline changes
• Experience with data quality tooling/platforms (advantage), e.g., Syniti or Monte Carlo
• Experience with SAP and/or integration architecture (advantage)
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The offer that would convince me:

• A constantly growing organization and increasing opportunities
• Secure, long-term job opportunity
• Varied and engaging job responsibilities
• Outstanding salary
• Flexible work arrangements
• Home office possibility 

Online application:

Please use our online application and attach your resume.

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