Data Quality Developer

MUNKAVÉGZÉS HELYE 1112 Budapest, Boldizsár utca 2. TEVÉKENYSÉGI TERÜLET IT MUNKAVÉGZÉS KEZDETE Megegyezés szerint FOGLALKOZTATÁS MÉRTÉKE Teljes munkaidő megállapodás szerint .profile_item { list-style-type: none !important; } .profile_heading { font-weight: bold; } .profile_content { padding-bottom: 0.5rem; } .profile_section:has(.profile_item:empty) { display: none; } 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) Good command of English (upper-intermediate level or higher) Ability to work in an agile environment 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. AIIS Adatkezelési tájékoztató Privacy notice