Senior Data Engineer (Azure, Databricks & MLOps)

• A senior role with technical influence and responsibility:
• A salary appropriate to your experience and role
• A working week of 32 to 40 hours
• 24 vacation days based on full-time employment with the possibility to purchase 6 additional days (pro rata);
• Hybrid working with a lot of flexibility in workplace and hours
• Extensive training and development opportunities via, among others, the VINCI Energies Academy, the Axians Institute, and online learning platforms.
• Growth opportunities towards, for example, lead consultant, business consultant, or strategic advisor
• Lease car or mobility budget, laptop, and smartphone
• Good secondary employment conditions, including a pension scheme and share program
• An open, inclusive, and collegial working environment

As a Senior Data Engineer at Axians, you will work on designing, building, and optimizing scalable data and ML platforms for clients within sectors including finance and the public sector. You will work in multidisciplinary teams alongside fellow engineers, data scientists, analysts, and stakeholders on challenging data issues.

You combine deep technical knowledge with a leadership role: you help clients and colleagues move forward by providing direction, introducing best practices, and taking the technical lead in design and implementation choices.

Your responsibilities include, among other things:

• Designing and developing scalable data pipelines and production-level ML pipelines
• Modeling and structuring data for analytics and reporting
• Building, optimizing, and further developing data platforms in the cloud
• Integrating various data sources into robust architectures
• Improving data quality, performance, and reliability
• Advising clients on data architecture, modeling, and best practices
• Building and maintaining ML workflows, including training, validation, deployment, and monitoring
• Applying CI/CD and automation for data and ML workflows
• Actively involving and coaching colleagues and client teams in technical choices and working methods

In doing so, you make use of modern data engineering tooling and cloud environments such as Azure, Databricks, and (where relevant) Snowflake.

You will work with, among others:

• Databricks (PySpark and SQL), including Databricks for ML workloads
• Python (solid programming skills) and SQL (complex queries and performance optimization)
• Spark or similar large-scale processing frameworks
• ML experiment tracking and model management (e.g., MLflow or equivalent)
• Azure Data Factory for orchestration and integration
• dbt for transforming and modeling data according to software engineering best practices
• CI/CD, Git, and DevOps principles for data and ML workflows
• Data modeling and architecture principles such as Kimball and Data Vault
• Cloud environments such as Azure (experience with AWS or GCP is also valuable)