Data Engineer with DevOps focus for Justice digitalisation

What you will do

You will join the development team working on the digitalisation of justice processes, specifically supporting projects that automate the handling of traffic fines and related procedures. As a data engineer with a strong DevOps profile for BI, you will bridge the gap between business requirements and technical delivery.

Your responsibilities include:

• Translate standards, norms and guidelines (architecture, information security, GDPR, internal IT policies) into concrete functional and technical requirements for data flows and reporting

• Work with the Product Owner and functional analysts to gather information needs and develop them into user stories with clear acceptance criteria

• Define data definitions and KPIs unambiguously (data dictionary, semantic layer) so that each KPI is calculated consistently everywhere

• Act as the point of contact within the team for data and BI questions, coordinating with developers, architects, testers and stakeholders

Data quality

• Develop, automate and monitor data quality rules (completeness, accuracy, timeliness, consistency)

• Build controls into loading processes; detect, report and follow up on anomalies until resolved

• Maintain lineage and documentation of data models, data flows and processes

• Report on data quality to the team, Product Owner and management

BI DevOps

• Set up and manage CI/CD for data pipelines, data models and reports: version control (Git), code reviews, automated tests and controlled releases across development, test and production environments

• Set up monitoring and alerting for batch processes and data loads

• Help monitor quality gates for data code and reports

• Support release and change management and help resolve incidents

Data engineering

• Develop and maintain ETL/ELT processes

• Provide a reliable publication layer for BI tools (including SAS Visual Analytics, Power BI, Apache Superset)

• Contribute to thinking about the evolution of the data platform and, where appropriate, make logic tool-independent

• Make data AI-ready: ensure that datasets are well documented, of high quality and securely accessible so they can serve as a basis for future AI applications

Governance and compliance

• Help ensure traceability of data, decisions, code and tests, in line with internal control and audit expectations

• Contribute to compliance with information security, GDPR and access management from source to report (minimal rights, row-level security)