Data Engineer

Bridgestone Mobility Solutions

Data Engineer AI Platform (Databricks)

About Bridgestone Mobility Solutions (BMS)

We are Bridgestone Mobility Solutions, the digital product factory for the largest global tire company, and we are a team committed to product innovation in the transportation and mobility space.

With work spanning everything from tire integrated sensor and roadway vision recognition R&D to building mobile service and commercial vehicle marketplaces, we are defining the interconnected future of digital vehicles.

We are a team with a strong commitment to customer-driven innovation, data-based decision-making, and a commitment to learning through experimentation.

As a part of the Bridgestone team, the opportunities are endless across a broad spectrum of businesses in the Bridgestone portfolio.

Our Culture of learning and growth has enabled our Engineers to grow in leaps and bounds in the last decade. We are dedicated to having our engineering teams span the entire value chain from customer need to engineering to understand every aspect of the product teams job

Job brief

Bridgestone GCC is building a scalable AI platform to power mobility, retail, fleet, and enterprise use cases.

We are looking for a Data Engineer AI Platform to design and manage data pipelines, knowledge ingestion frameworks, and scalable Lakehouse architecture on Databricks that enable GenAI Engineers and ML teams to build production-grade AI solutions.

This role is critical in ensuring enterprise data is reliable, governed, AI-ready, and optimized for large-scale RAG and ML workloads.

Responsibilities:

Databricks Platform Engineering

• Design and maintain scalable Lakehouse architecture using Databricks.
• Implement Delta Lake pipelines (batch + streaming).
• Manage workspace structure, Unity Catalog, and cluster policies.
• Optimize compute usage and cost efficiency.

AI Data Enablement (RAG & ML Support)

• Build data ingestion pipelines for structured and unstructured data.
• Enable document ingestion and embedding pipelines for GenAI solutions.
• Maintain vector indexing pipelines for enterprise knowledge.
• Build reusable feature pipelines for ML models.

Data Governance & Security

• Implement governance using Unity Catalog.
• Define access control, role-based permissions.
• Ensure compliance with enterprise security standards.
• Enable auditability of AI data usage.

Collaboration with AI/ML Engineers

• Work closely with GenAI Engineers to:
• Prepare AI-ready datasets
• Support RAG frameworks
• Optimize feature engineering
• Enable MLflow experiment tracking and model data dependencies.

Experience

• 4 + years in Data Engineering
• 2+ years in Databricks environment
• Experience working with AI/ML teams preferred

Technical Requirements:

Core Platform Skills

• Databricks Lakehouse
• PySpark
• SQL
• Delta Lake
• Unity Catalog

Data Engineering

• ETL/ELT pipelines
• Structured & unstructured data processing
• Streaming pipelines (Kafka / Spark Streaming)
• Data modeling

Cloud

• AWS or Azure
• S3