Functieomschrijving

Sector: Telecommunications
Employment Type: Contract
Location: Belgium (Hybrid)
As a Senior Data Quality Developer, you will play a key role in strengthening enterprise data quality across multiple business domains. You will develop robust data quality controls, support large-scale IT and migration projects, and implement automated reconciliation processes that improve data reliability and business decision-making.
Working with modern data technologies and best practices, you will contribute to enterprise-wide data governance while ensuring high-performance, scalable, and maintainable data quality solutions.

Key responsibilities

• Data quality development:
  • Design, develop, and maintain enterprise data quality solutions.
  • Build automated data quality checks using Python and Apache Spark.
  • Translate business requirements into scalable technical solutions.
  • Estimate development effort and technical feasibility for new requests.
  • Apply enterprise data quality methodologies and best practices.
  • Ensure solutions meet performance, scalability, and availability requirements.
• Data quality framework & governance:
  • Develop and enhance the organization's Data Quality Framework.
  • Document data quality rules and technical assets within the enterprise data governance platform.
  • Ensure compliance with internal architecture, governance, and development standards.
  • Promote data quality best practices across development teams.
• Data integration & reconciliation:
  • Develop end-to-end reconciliation processes for enterprise data.
  • Integrate data from multiple internal and external sources.
  • Build consolidated data models for downstream processing.
  • Implement reconciliation rules defined by business analysts.
  • Produce consolidated datasets including issue categorization and audit information.
  • Maintain historical audit trails to monitor data evolution over time
• Data engineering:
  • Design and optimize large-scale data processing jobs.
  • Develop high-performance ETL and data integration processes.
  • Monitor data pipelines and job dependencies.
  • Perform root cause analysis for processing failures.
  • Conduct impact assessments and implement enhancements as business requirements evolve
• Reporting & analytics support:
  • Develop statistical and reporting datasets.
  • Build reusable data assets that support business reporting.
  • Perform complex SQL queries and data analysis.
  • Support enterprise data cleansing and data quality initiatives.
• IT project support:
  • Support enterprise IT transformation and migration projects with a strong data quality component.
  • Collaborate with Agile delivery teams throughout the project lifecycle.
  • Participate in:
    • Sprint Planning
    • Backlog Refinement
    • Peer Reviews
    • Retrospectives
    • Release Planning
  • Communicate progress, risks, and deliverables to technical and business stakeholders.

Key functional areas

• Joint venture end-to-end audit:
  • Load and integrate external data files into enterprise data models.
  • Implement reconciliation logic.
  • Build consolidated audit datasets.
  • Maintain historical audit tracking.
  • Produce reporting and statistical tables.
  • Monitor processing workflows and dependencies.
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