Senior Data Engineer

pThis 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. /ph3Main responsibilities /h3h3Requirements And Standards — Bridging Role /h3ulliTranslate norms, standards and directives (architecture, information security, GDPR, internal IT policies) into concrete functional and technical requirements for data flows and reporting /liliIn collaboration with the Product Owner and functional analysts, identify information needs and translate them into user stories with clear acceptance criteria /liliUnambiguously define data definitions and KPIs (data dictionary, semantic layer), so that every KPI is calculated in the same way everywhere /liliBe the go-to person within the team for data and BI issues, and coordinate with developers, architects, testers, and stakeholders /li /ulh3Data quality /h3ulliDevelop, automate and monitor data quality rules (completeness, accuracy, timeliness, consistency) /liliIntegrate controls into loading processes; detect, report and follow up on anomalies until they are resolved /liliRecord traceability and document data models, data flows, and processes /liliReport on data quality to the team, Product Owner, and management /li /ulh3BI DevOps /h3ulliImplement 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 /liliImplement a monitoring and alerting system for batch processes and data loads /liliParticipate in the monitoring of quality gates for data code and reports /liliManage releases and changes, and participate in the resolution of incidents /li /ulh3Data engineering /h3ulliDevelop and maintain ETL/ELT processes /liliProvide a reliable publishing layer for BI tools (including SAS Visual Analytics, Power BI, Apache Superset) /liliParticipate in the reflection on the evolution of the data platform and, where relevant, make the logic independent of the tool /liliPreparing 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 /li /ulh3Governance and compliance /h3ulliHelp ensure traceability of data, decisions, code, and tests, in accordance with internal control and audit requirements /liliContribute to compliance with information security rules, GDPR and access management, from source to report (minimum rights, line-level security) /li /ulh3Education And Experience /h3ulliMaster's or bachelor's degree in ICT or similar orientation based on experience /liliAt 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 /liliProven experience in translating business and quality requirements into requirements and acceptance criteria for data (data as a product) /liliExperience in setting up and monitoring data quality controls /li /ulh3Certifications (asset) /h3ulliAsset: DAMA CDMP or any other data management or data quality certification /liliAsset: Certification as a cloud data engineer (e.g., Microsoft, Databricks) or DevOps-related certification /li /ulh3Technical knowledge /h3ulliExpert-level SQL and data modeling (Kimball and/or Data Vault, SCD, incremental loads) /liliExperience with ETL/ELT processes and batch processing; proficiency in SAS 9.4 (Base SAS, Enterprise Guide, DI Studio) is an asset /liliVersion management (Git), branching policies, code reviews, and CI/CD tools (e.g., GitHub Actions, Azure DevOps, GitLab CI) /liliTest automation and data quality control tools (e.g., dbt tests or equivalents) /liliMonitoring and alerting of data processing processes /liliKnowledge of BI tools (SAS Visual Analytics, Power BI and/or Apache Superset) and the principle of a semantic layer /liliAsset: Python, dbt, Docker, Azure /liliEffective use of Jira/Azure Boards /li /ulpSkills: etl,azure,sas /p