Senior Data Engineer – Real-Time & Distributed Systems (GCP)

Who we are:

Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are the AI technology solutions provider-of-choice to 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.

By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of clean and optimized digital data to all industries. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.

Our global workforce includes over 3,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years.

Key Responsibilities

• 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

Preferred Skills

• Proficiency in Go, Java, or Scala

• Experience with Kafka or Flume for streaming ingestion

• Deep familiarity with the Google Cloud Platform ecosystem

• Experience with production monitoring, logging, and observability frameworks

• Exposure to high-availability, multi-region deployments

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