Data Quality Developer

ALDI aldi-hofer.com Open · verified Oct 24, 2026

Debrecen Mid-level Data Architecture & Governance

At a glance

Mid-level Data Architecture & Governance role at ALDI. Debrecen.

Pay not stated Data Architecture & Governance salary

RoleData Architecture & Governance

SeniorityMid-level

LocationDebrecen

PostedSep 27, 2026 · 3w ago

Role brief

dataroles summary, based on the employer's posting.

What you'll do

Build checks across master, transactional, in-transit, reconciliation, and analytical data
Set up table monitors for pipeline delays, freshness, expected volumes, schema shifts, and other data health problems
Create KPI and validation monitors to catch anomalies, reconcile systems, check tolerances, and flag invalid rows
Track slow or risky queries, then add monitoring to data pipelines and maintain it through operation
Work with engineering and analytics teams to trace incidents, prevent recurrence, and document alert meaning and ownership

What you bring

You have practical experience writing quality rules and running monitoring for structured data
Strong SQL and Python skills, plus knowledge of data management, quality, and architecture
You understand how to turn completeness, accuracy, consistency, uniqueness, validity, and timeliness into checks
You can communicate and collaborate across data engineering, data product, and analytics or reporting teams

Who this fits

This mid-level role suits someone who can build and operate data quality monitoring and work across technical and analytics teams. The position is full-time and based at the stated Debrecen address.

From the employer

MUNKAVÉGZÉS HELYE
4024 Debrecen, Barna utca 23

TEVÉKENYSÉGI TERÜLET
IT

MUNKAVÉGZÉS KEZDETE
Megegyezés szerint

FOGLALKOZTATÁS MÉRTÉKE
Teljes munkaidő

megállapodás szerint

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)