Senior Data Engineer

This person is part of the development team and acts as the link between the requirements (standards, guidelines and business information needs) and what is delivered on a technical level.

The data engineer translates standards and requests into clear requirements, ensures data quality throughout the chain, and ensures that data from source to report is reliable, verifiable, and reproducible. In this context, DevOps principles are applied to BI: everything is version-controlled, tested in an automated way and deployed in a controlled manner.

This profile is not linked to a particular technology: the data engineer works with the existing environment (in particular SAS) and participates in the reflection on the evolution of the data platform.

This focus on reliable, well-defined and quality data also makes this profile an important enabler for AI. Applications such as predictive analytics, automatic classification, or AI-assisted case processing are entirely dependent on the quality, traceability, and accessibility of the underlying data. It is the data engineer who lays the foundations.

4. Main responsibilities:

Requirements and standards — bridging role

• Translate norms, standards and directives (architecture, information security, GDPR, internal IT policies) into concrete functional and technical requirements for data flows and reporting
• In collaboration with the Product Owner and functional analysts, identify information needs and translate them into user stories with clear acceptance criteria
• Unambiguously define data definitions and KPIs (data dictionary, semantic layer), so that every KPI is calculated in the same way everywhere
• Be the go-to person within the team for data and BI issues, and coordinate with developers, architects, testers, and stakeholders

Data quality

• Develop, automate and monitor data quality rules (completeness, accuracy, timeliness, consistency)
• Integrate controls into loading processes; detect, report and follow up on anomalies until they are resolved
• Record traceability and document data models, data flows, and processes
• Report on data quality to the team, Product Owner, and management

BI DevOps

• Implement and manage CI/CD for data pipelines, data models, and reporting: Git versioning, code reviews, automated testing, and controlled releases in development, test, and production environments
• Implement a monitoring and alerting system for batch processes and data loads
• Participate in the monitoring of quality gates for data code and reports
• Manage releases and changes, and participate in the resolution of incidents

Data engineering

• Develop and maintain ETL/ELT processes
• Provide a reliable publishing layer for BI tools (including SAS Visual Analytics, Power BI, Apache Superset)
• Participate in the reflection on the evolution of the data platform and, where relevant, make the logic independent of the tool
• Preparing data for AI: Ensuring that datasets are well-documented, high-quality, and securely accessible, so that they can serve as the basis for future AI applications

Governance and compliance

• Help ensure traceability of data, decisions, code, and tests, in accordance with internal control and audit requirements
• Contribute to compliance with information security rules, GDPR and access management, from source to report (minimum rights, line-level security)

5. Qualifications and skills required:

Education and experience

• Master's or bachelor's degree in ICT or similar orientation based on experience
• At least 5 years of relevant experience in the field of data engineering, data storage or BI, including at least 2 years in a development team working with CI/CD
• Proven experience in translating business and quality requirements into requirements and acceptance criteria for data (data as a product)
• Experience in setting up and monitoring data quality controls

Certifications (asset)

• Asset: DAMA CDMP or any other data management or data quality certification
• Asset: Certification as a cloud data engineer (e.g., Microsoft, Databricks) or DevOps-related certification

Technical knowledge

• Expert-level SQL and data modeling (Kimball and/or Data Vault, SCD, incremental loads)
• Experience with ETL/ELT processes and batch processing; proficiency in SAS 9.4 (Base SAS, Enterprise Guide, DI Studio) is an asset
• Version management (Git), branching policies, code reviews, and CI/CD tools (e.g., GitHub Actions, Azure DevOps, GitLab CI)
• Test automation and data quality control tools (e.g., dbt tests or equivalents)
• Monitoring and alerting of data processing processes
• Knowledge of BI tools (SAS Visual Analytics, Power BI and/or Apache Superset) and the principle of a semantic layer
• Asset: Python, dbt, Docker, Azure
• Effective use of Jira/Azure Boards