Senior Data Quality Developer

Senior Data Quality Developer

Sector:
Telecommunications

Employment Type:

Contract

Location:

Belgium (Hybrid)

Mission Overview

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 Area

s

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.

Customer Device Audit

Integrate data from multiple enterprise source systems.

Develop data quality and reconciliation processes.

Support data cleansing initiatives.

Deliver actionable insights through optimized SQL queries.

IoT Services Audit

Audit IoT services using metadata-driven methodologies.

Develop efficient processing jobs for large-scale datasets.

Improve processing performance and scalability.

Geospatial Data Quality

Perform consistency and quality validation between enterprise master data and data warehouse information, including:

Geographic addresses

Buildings

Building units

Geospatial reference information

Required Qualifications

Master's degree in:

Business Analytics

Industrial Engineering

Data Science

Computer Science

Information Technology

Or equivalent professional experience

Professional Experience

Minimum 8 years of experience developing enterprise data solutions.

Strong background in data quality, data engineering, or enterprise data management.

Experience delivering large-scale data-driven IT solutions.

Telecommunications industry experience is considered an advantage.

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