Senior Data Engineer

As a Senior Data Engineer (L3) joining our high-performing Data Science team, you will play a crucial role in building scalable data infrastructure, developing robust data pipelines, and enabling advanced analytics and machine learning workflows. Your expertise in modern data engineering technologies, including DBT, Databricks, PySpark, Airflow, SQL, and cloud-based data systems, will be essential as you collaborate with Data Science, Analytics, and Engineering teams across global locations. Responsibilities: - Design, develop, and maintain scalable DBT models for analytics, reporting, and machine learning use cases. - Build and optimize data pipelines using Databricks, PySpark, Delta/Iceberg tables, and SQL. - Develop reliable orchestration workflows using Airflow and Prefect. - Transform raw operational data into trusted, production-grade datasets. - Collaborate with Data Scientists and ML Engineers to productionize feature pipelines and ML-supporting datasets. - Improve data quality, reliability, testing, and documentation standards. - Optimize Spark and DBT workloads for performance, scalability, and cost efficiency. - Participate in peer code reviews and contribute to engineering best practices. - Work closely with cross-functional global teams to support operational and business decision-making. Requirements: - 5+ years of experience in Data Engineering. - Strong expertise in SQL and distributed data systems. - Hands-on experience with DBT, Databricks, PySpark / Spark, Airflow, Prefect, and Delta Lake / Iceberg. - Experience with Infrastructure-as-Code tools such as Terraform, AWS CDK, or Pulumi. - Good understanding of data architecture, scalability, performance optimization, and fault tolerance. - Experience working with cloud-based modern data platforms. - Strong communication and collaboration skills. Preferred Skills: - Experience with streaming systems like Kinesis. - Exposure to MLOps and AI/ML workflows. - Familiarity with AI coding assistants such as Copilot, Cursor, Claude Code, etc. - Experience working with globally distributed teams. - Understanding of the supply chain or logistics domain is an added advantage. In this role, we are looking for individuals who are self-driven, proactive, possess strong problem-solving abilities, can thrive in a fast-paced, collaborative environment, and have a passion for building scalable and reliable data systems. As a Senior Data Engineer (L3) joining our high-performing Data Science team, you will play a crucial role in building scalable data infrastructure, developing robust data pipelines, and enabling advanced analytics and machine learning workflows. Your expertise in modern data engineering technologies, including DBT, Databricks, PySpark, Airflow, SQL, and cloud-based data systems, will be essential as you collaborate with Data Science, Analytics, and Engineering teams across global locations. Responsibilities: - Design, develop, and maintain scalable DBT models for analytics, reporting, and machine learning use cases. - Build and optimize data pipelines using Databricks, PySpark, Delta/Iceberg tables, and SQL. - Develop reliable orchestration workflows using Airflow and Prefect. - Transform raw operational data into trusted, production-grade datasets. - Collaborate with Data Scientists and ML Engineers to productionize feature pipelines and ML-supporting datasets. - Improve data quality, reliability, testing, and documentation standards. - Optimize Spark and DBT workloads for performance, scalability, and cost efficiency. - Participate in peer code reviews and contribute to engineering best practices. - Work closely with cross-functional global teams to support operational and business decision-making. Requirements: - 5+ years of experience in Data Engineering. - Strong expertise in SQL and distributed data systems. - Hands-on experience with DBT, Databricks, PySpark / Spark, Airflow, Prefect, and Delta Lake / Iceberg. - Experience with Infrastructure-as-Code tools such as Terraform, AWS CDK, or Pulumi. - Good understanding of data architecture, scalability, performance optimization, and fault tolerance. - Experience working with cloud-based modern data platforms. - Strong communication and collaboration skills. Preferred Skills: - Experience with streaming systems like Kinesis. - Exposure to MLOps and AI/ML workflows. - Familiarity with AI coding assistants such as Copilot, Cursor, Claude Code, etc. - Experience working with globally distributed teams. - Understanding of the supply chain or logistics domain is an added advantage. In this role, we are looking for individuals who are self-driven, proactive, possess strong problem-solving abilities, can thrive in a fast-paced, collaborative environment, and have a passion for building scalable and reliable data systems.