Overview

We turn customer challenges into growth opportunities. Material is a global strategy partner to the world’s most recognizable brands and innovative companies. Our people around the globe thrive by helping organizations design and deliver rewarding customer experiences.

Responsibilities

• Design and build scalable data platforms using Databricks, Spark, Delta Lake, and PySpark.
• Develop and optimize large-scale data pipelines and distributed processing applications.
• Perform Spark performance tuning, query optimization, and troubleshooting.
• Build and manage CI/CD pipelines using GitLab and automation tools.
• Work on REST APIs, Databricks automation, and deployment workflows.
• Support AI/ML workflows including LLMOps, RAG pipelines, vector search, and model evaluation.
• Implement data governance, security, metadata management, and data quality standards.
• Conduct code reviews, mentor junior engineers, and drive engineering best practices.
• Collaborate with DevOps, ML, and Business teams to deliver scalable AI and analytics platforms.
• 5+ years of hands-on experience in Data Engineering with strong enterprise delivery experience.
• Proven experience as a Senior Data Engineer / Lead Data Engineer in large-scale environments.
• Expert-level programming experience in Python and PySpark.
• Strong expertise in Apache Spark internals and Spark performance optimization.
• Hands-on experience with:
• Databricks
• Delta Lake
• Spark SQL
• Advanced SQL optimization
• Extensive experience building and maintaining enterprise CI/CD pipelines.
• Strong expertise in GitLab pipelines and automated deployment workflows.
• Hands-on experience with Databricks REST APIs.
• Strong understanding of distributed data processing and scalable architecture design.

Qualifications

• Strong experience in scalable data integration and distributed processing frameworks.
• Strong analytical, debugging, and problem-solving capabilities.
• Excellent stakeholder communication and technical leadership skills.