Senior Data Engineer – Real-Time & Distributed Systems, GCP

Role Overview

• Design, build, and optimize scalable data pipelines for batch and real-time processing
• Develop and maintain event-driven architectures for high-throughput systems
• Ensure data reliability, performance, and low-latency processing across distributed environments
• Collaborate with data scientists and application teams to enable analytics and AI use cases
• Implement best practices in performance tuning, monitoring, and cost optimization

Requirements

• Advanced proficiency in Python for backend and large-scale data processing
• Strong experience building and managing big data pipelines in production environments
• Hands-on expertise with workflow orchestration tools such as Airflow or Google Cloud Composer
• Proven experience in batch and streaming data processing using: Apache Spark Apache Beam (Dataflow)
• Experience designing and operating event-driven systems using Pub/Sub
• Strong understanding of distributed systems architecture and scalability patterns
• Experience managing globally distributed, low-latency datasets
• Hands-on experience with NoSQL databases and/or Google Cloud Spanner
• Strong knowledge of system reliability, fault tolerance, and performance optimization

Tech Stack

• Airflow
• Apache
• Cloud
• Distributed Systems
• NoSQL
• Python
• Spark