# Data Platform Engineer

**Company**: AVL
**Location**: Sala Al Jadida, MA
**Work arrangement**: onsite
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Automotive
**Wikidata**: https://www.wikidata.org/wiki/Q300157

**Apply**: https://jobs.avl.com/job/Sala-Al-Jadida-Data-Platform-Engineer/1365823133/
**Canonical**: https://yubhub.co/jobs/job_42c9cfa4-8e3

## Description

### 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!

## Skills

### Required
- Databricks
- Foundry
- Python
- SQL
- TypeScript
- Spark
- PostgreSQL
- CI/CD
- Git
- ETL
- performance optimisation
- scalable architecture design

### Nice to have
- cloud-native ML
- MLOps