Description :

Job Title : Data Engineer

Department : Global Analytics

Reports to : Manager AI Engineering

Level : Senior / Medior (depending on experience)

Role Summary :

The Data Engineer is responsible for designing, building, and operating high-quality, scalable, and reusable data services that support analytics, AI, and GenAI use cases across business domains.

In this role, you will design and work hands-on with data pipelines, data models, orchestration frameworks, storage layers, and observability tooling.

You will collaborate closely with AI Engineers, Data Scientists, Product Owners, and Platform teams to deliver reliable, well-governed, and self-service data products.

Key Responsibilities :

Data Platform & Services Engineering :

- Build and maintain scalable data pipelines and ingestion frameworks for batch, streaming, and event-driven data.

- Develop and maintain modular data models and semantic layers optimized for analytics, BI self-service and AI use cases.

- Implement and operate orchestration workflows (e.g., Databricks Workflows) and compute engines (Spark, SQL, Python).

- Work with storage technologies such as Delta Lake, ADLS, feature and vector stores.

Data Quality, Governance & Observability :

- Implement data quality checks, validations, and monitoring to ensure reliability and trust in data products.

- Contribute to data lineage, metadata management, and documentation.

- Apply observability practices using tools such as Great Expectations or Monte Carlo.

- Ensure compliance with data governance standards and regulations (e.g., GDPR) in collaboration with data governance teams.

Enablement for AI & Analytics Use Cases :

- Deliver curated datasets and reusable data assets for analytics, machine learning, and GenAI applications.

- Build pipelines that process structured, graph, and unstructured data (e.g., text, documents, images).

- Support AI Engineering teams with data preparation for embeddings, vector stores, and retrieval-augmented generation (RAG) pipelines.

Tooling & Self-Service :

- Contribute to data engineering tooling and frameworks that enable e icient development and deployment of pipelines.

- Develop data pipelines using tools such as dbt and Databricks Lakeflow.

- Support reuse of data services through clear documentation, data contracts, templates, and examples.

Collaboration & Ways of Working :

- Collaborate with Data Scientists, AI Engineers, Product Owners, Business SMEs, and Platform teams.

- Participate in technical design discussions, code reviews, and architecture forums.

- Follow engineering best practices for version control, testing, CI/CD, and operational excellence.

Preferred Qualifications :

- 5+ years of experience in data engineering and building production-grade data pipelines.

- Strong hands-on experience with data platforms such as Databricks.

- Solid knowledge of data modeling, SQL, Spark, and Python.

- Experience with orchestration frameworks, data quality tooling, and observability practices.

- Exposure to unstructured data processing and AI/GenAI data pipelines is a strong plus.

- Experience working in a global, multi-team environment is beneficial.

Success in This Role Means :

- Reliable, well-documented data products are available for analytics and AI use cases.

- Data pipelines are scalable, cost-efficient, observable, and easy to operate.

- Data engineers and AI teams can move faster using reusable patterns and self service data services.

- Structured and unstructured data are effectively integrated to support advanced analytics and GenAI innovation.