Freelance Data Analyst

For one of our clients in Namur, Belgium, we are looking for an experienced Data Analyst for a long-term freelance assignment.

Start date: October 2026

Duration: 660 working days

Location: Namur, Belgium

Remote work: 50%

Language: French – excellent written and spoken level required

Contract: Freelance

Context

Our client is strengthening the use, sharing and governance of data across its organisation. The structured, secure and documented sharing of data has become a strategic priority.

A dedicated data-sharing platform is used to facilitate the distribution of both geographic and non-geographic data, while ensuring data quality, compliance, documentation and long-term sustainability.

The Data Analyst is involved throughout the entire data lifecycle, from analysing business requirements to data publication, documentation, quality monitoring and maintenance.

The role also contributes to application development projects at an early stage by applying data-centric principles to ensure that data is properly structured, reusable, interoperable and ready for future sharing.

The Data Analyst must be able to analyse datasets, assess their maturity and ensure compliance with applicable standards, regulations and internal data governance rules.

The position combines technical, analytical and communication skills. Strong understanding of business requirements and the ability to communicate with Data Owners, Data Managers, technical teams and end users are essential.

Main Objectives

- Ensure legal and technical compliance with requirements such as INSPIRE, High-Value Datasets (HVD), Open Data and internal data standards.
- Guarantee data quality, reliability and traceability through appropriate validation and monitoring mechanisms.
- Industrialise and automate data sharing by transforming production data (vPROD) into usable and regularly updated distribution data (vDIFF) using FME and/or Denodo.
- Improve data accessibility and discoverability through high-quality metadata and appropriate publication channels such as geoportals, APIs, web services and GIS platforms.
- Integrate data-centric principles into application development projects.
- Strengthen data governance by clearly defining responsibilities between data producers, Data Owners and Data Managers.

Key Concepts

Data Committee: A governance group responsible for coordinating and prioritising data-sharing activities.

Data-sharing platform: A solution used for reading source data, documentation, modelling, quality control and transformation.

Data Owner: The business authority responsible for a dataset, including its business rules, access conditions, quality, compliance and security requirements.

Data Manager: The entity responsible for the operational management of data, including production, quality, integrity, storage, updates, documentation and distribution.

vPROD: The source version of a dataset produced and maintained by the Data Manager.

vDIFF: The transformed distribution version made available to the target audience.

Metadata platform: The organisation's solution for managing and maintaining dataset metadata.

Responsibilities

1. Data Request Assessment

- Analyse incoming data-sharing requests and application-related data requirements.
- Assess relevance, priorities and applicable European or legal requirements.
- Assign datasets to Data Analysts.

2. Contact with Data Managers

- Inform the Data Manager that the dataset is being prepared for sharing.
- Explain the overall process, requirements and support available.

3.



Project Planning

Prepare a project plan covering:

- Target users
- Objectives of data sharing
- Source data and access points
- Update frequency
- Access and usage conditions
- Open Data or other licensing requirements
- Stakeholders
- Target platforms and publication channels

4. Metadata

- Create and maintain the dataset metadata in collaboration with the metadata team and Data Manager.
- Ensure metadata is complete, accurate and up to date.

5. Source Data Analysis

Analyse the vPROD dataset, including:

- Structure
- Content
- Existing documentation
- Data maturity
- Quality issues
- Potential compliance issues

If the dataset is not sufficiently mature for publication, coordinate with the relevant data governance stakeholders and guide the Data Manager towards the appropriate support teams.

6. Data Modelling

Design and document the vDIFF distribution model, including:

- Tables and attributes
- Names and aliases
- Descriptions
- Value lists
- Constraints
- Access conditions
- Technical specifications

Ensure the resulting model supports interoperability and future reuse.

7. Data Transformation

Develop FME workspaces and/or Denodo transformations to automatically convert the source vPROD model into the vDIFF distribution model.

The transformation must be reusable and automatically executable when the source data is updated.

8. Compliance & Quality Control

- Validate the vDIFF dataset against the defined technical specifications.
- Identify, analyse and document errors.
- Discuss issues with Data Managers and coordinate corrective actions.
- Ensure compliance with applicable data standards and governance requirements.

9. Web Services & GIS

For geographic datasets:

- Prepare and configure mapping/web-service projects using ArcGIS Pro.
- Apply appropriate GIS and cartographic best practices.
- Ensure the solution meets the requirements of the Data Manager and target users.

For non-geographic datasets:

- Support publication through Denodo web services and other appropriate interfaces.

10. Validation

- Coordinate validation of deliverables with the Data Manager.
- Organise cross-validation with other Data Analysts where required.
- Ensure all deliverables meet the agreed specifications.

11. Data Management Agreement

Prepare the agreement with the Data Manager defining responsibilities regarding:

- Data updates
- Metadata reviews
- Quality checks
- Data maintenance
- Notification of decommissioning
- Other governance commitments

12. Publication

- Create the necessary publication requests/tickets.
- Coordinate publication of datasets and associated deliverables within the shared data infrastructure.4

13. Reporting

Provide regular reporting on:

- Progress
- Priorities
- Risks
- Issues
- Dependencies
- Required decisions

14. Communication

Coordinate with the relevant communication stakeholders when datasets are published or updated.

15. Data Maintenance

Ensure published data remains up to date by regularly checking:

- Dataset content
- Metadata
- Download links
- Web services
- Update status
- Overall validity of the published information

Work closely with Data Managers to ensure continuous maintenance.





16. Support

Provide support and guidance to:

- End users
- Data Managers
- Technical teams
- Owners of the data-sharing infrastructure

Respond to questions and issues related to data sharing, publication and data quality.

Must-Have Technical Skills

- Strong experience as a Data Analyst.
- Strong expertise in Data Modelling.
- Strong experience in Data Quality Management and Data Validation.
- Experience with Data Governance and data lifecycle management.
- Experience with Metadata Management and technical documentation.
- Experience with Geospatial Data / GIS.
- Experience with Data Virtualisation.
- Hands-on experience with FME.
- Hands-on experience with Denodo or a comparable data virtualisation solution.
- Experience with ArcGIS Pro or an equivalent GIS platform.
- Good understanding of ETL/data transformation processes.
- Understanding of data interoperability and data reusability.
- Knowledge of INSPIRE, HVD and Open Data requirements.
- Ability to translate business requirements into data models and technical specifications.
- Experience with APIs, web services and data publication is an advantage.

Must-Have Language

French – Mandatory

- Excellent spoken and written French.
- Strong communication and active listening skills.
- Ability to communicate effectively with both technical and business stakeholders.

Soft Skills

- Dynamic and adaptable personality.
- Strong communication and assertiveness.
- Pragmatic and solution-oriented approach.
- Excellent analytical and problem-solving skills.
- Strong writing and documentation capabilities.
- Service- and customer-oriented mindset.
- Able to quickly understand business needs and priorities.
- Comfortable working with different types of stakeholders.
- Able to work independently while collaborating effectively with a wider team.
- Strong sense of responsibility and ownership.

Key Technologies & Keywords

Data Analysis | Data Modelling | Data Quality | Data Governance | Metadata | Data Virtualisation | FME | Denodo | ArcGIS Pro | GIS | Geospatial Data | ETL | Data Transformation | APIs | Web Services | Open Data | INSPIRE | HVD

Evaluation Criteria

The profile will be evaluated according to the following weighting:

35% – Technical skills: Data Modelling & Data Quality

Assessment based on the CV, with a focus on data modelling, validation and quality control.

35% – Technical skills: Geomatics & Data Virtualisation

Assessment based on the CV, with a focus on GIS/geospatial data and data virtualisation.

30% – Proposed Working Methodology

Assessment based on a maximum one-page methodology note accompanying the CV, covering the candidate's understanding of:

- The business and data context
- The required tools and technologies
- Data governance
- Data quality
- Applicable legal and regulatory requirements
- Proposed approach and methodology

Each criterion is assessed on a 1–5 scale, with the final score calculated using the weighting above.

Ideal Candidate

The ideal candidate is a senior, versatile Data Analyst combining strong technical expertise in data modelling, data quality, GIS and data virtualisation with excellent communication and documentation skills.

You should be comfortable managing the complete data lifecycle, working with Data Owners and Data Managers, and translating complex business and regulatory requirements into practical, reusable and high-quality data solutions.

Experience in a public-sector, regulated or data-governance environment is a strong advantage.

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