Data Platform Engineer

Data Platform Engineer

Join AVL and make a direct impact on shaping the future of Data, AI, and Mobility.

Your Responsibilities:

• Review and stabilise existing platform implementations (Databricks, Foundry – pipelines, Ontology schemas, Workshop applications, Functions, notebooks).
• Identify performance bottlenecks, technical debt, and governance gaps across data pipelines and application layers.
• Lead Ontology governance and design reviews, acting as a gatekeeper for all schema changes (Object Types, Links, Properties, Actions).
• Define and document target data architectures (ingestion, transformation, and consumption layers).
• Establish coding standards, naming conventions, repository structures, and Function versioning policies.
• Enforce code reviews and technical validation before production deployment through Foundry Branching and Proposal workflows.
• Define and implement a structured testing strategy (unit tests for Functions, integration tests, data quality checks, pipeline expectations).
• Design and improve CI/CD pipelines and Dev/Test/Prod promotion processes using Foundry Marketplace/DevOps.
• Automate deployments, rollbacks, and environment configurations.
• Create and maintain architecture documentation (ADRs, data lineage diagrams, Ontology schemas, data flow diagrams).
• Design reusable Workshop component libraries, custom widgets, and Slate application patterns.
• Design and validate new platform solutions aligned with strategy, security, and governance requirements.
• Mentor the development team on architectural thinking and platform best practices (40% hands-on coding, 60% architecture/leadership).

Your Profile:

• Master’s degree in Computer Science, Data Engineering, or a related field.
• 5+ years of experience in data engineering or platform architecture roles.
• Strong expertise in modern data platforms (Databricks, Snowflake, AWS Glue, Azure Synapse, or similar). Foundry experience is strongly preferred but not required.
• Advanced skills in Python (PySpark), SQL (Spark SQL), and TypeScript for backend logic and application development.
• Experience with distributed data processing (Spark architecture, partitioning strategies, performance optimisation).
• Strong understanding of relational databases (PostgreSQL, Oracle, or similar).
• Experience with CI/CD workflows, Git branching strategies, and automated testing in data environments.
• Solid experience in end-to-end ETL and data transformation processes.
• Proven experience in performance optimisation and scalable architecture design.
• Experience in defining development standards, interface contracts, and engineering best practices.
• Hands-on coding mindset: must write production code daily, not only review or document.
• Structured, analytical, and documentation-oriented approach.
• Strong communication and technical leadership skills, with very good proficiency in English and French.

Benefits:

• A role with true technical ownership: architecture, scaling, and governance decisions that directly impact production AI solutions.
• Complex projects that go beyond “just pipelines” – covering big data processing and large-scale ML/DL deployment.
• Opportunities to deepen your expertise in Databricks, cloud-native ML, and MLOps.
• A team where your input and technical decisions truly matter.
• A competitive package and benefits.

How to Apply:

If you have these qualifications and are looking for a new challenge, we encourage you to apply to discuss it further!