Data Platform Engineer (GCP

Innodata(Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked.

Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted  provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

We are looking for a hands-on GCP / Data Platform Engineer to support, maintain, and enhance large-scale production data platforms running on Google Cloud. The role is focused on platform reliability, production support, troubleshooting, performance optimization, and continuous enhancement rather than greenfield platform development.

The ideal candidate can quickly understand a complex existing environment, diagnose issues across data pipelines and infrastructure, and implement practical improvements with minimal disruption. Experience operating large-scale, business-critical data platforms in complex enterprise environments is preferred.

• Support and maintain production GCP data platforms, pipelines, and workflows across batch and streaming workloads.
• Support production workflows orchestrated through Cloud Composer / Apache Airflow, including troubleshooting DAG failures, dependencies, scheduling issues, retries, and data-latency or SLA issues.
• Optimize Airflow DAGs and orchestration workflows to improve reliability, execution time, recoverability, and operational efficiency.
• Troubleshoot pipeline failures, data latency, performance degradation, configuration problems, and infrastructure-related issues.
• Work extensively with Cloud Spanner, Big Query, Dataflow, Pub/Sub, Cloud Composer (Apache Airflow), Cloud Storage, Cloud Monitoring and Logging, IAM, and related GCP services.
• Monitor platform health and proactively improve reliability, scalability, performance, and operational efficiency.
• Optimize existing pipelines and workloads to improve data refresh times, throughput, query performance, and platform stability.
• Support platform, infrastructure, configuration, and dependency upgrades while maintaining production stability and compliance.
• Perform root-cause analysis for recurring production issues and implement sustainable fixes.
• Support IAM, access controls, monitoring, alerting, logging, and operational governance.
• Collaborate with client and cross-functional Data Engineering, BI/Analytics, Application Engineering, Infrastructure, and Platform teams on production issues and enhancements.
• Adapt to established client-specific engineering, security, compliance, review, and operational processes.
• Review existing architectures and recommend incremental improvements without unnecessarily redesigning stable production systems.
• Create and maintain technical documentation, operational runbooks, troubleshooting guides, and platform support procedures.

• 3-7 years of experience in Data Engineering, Cloud Engineering, Platform Engineering, or a related role.
• Strong hands-on experience building, supporting, or maintaining production workloads on Google Cloud Platform (GCP).
• Strong experience with Big Query and Cloud Spanner.
• Hands-on experience with Dataflow, Pub/Sub, batch/streaming data pipelines, and Cloud Composer / Apache Airflow.
• Strong Python and SQL skills.
• Good understanding of GCP IAM, service accounts, permissions, monitoring, logging, alerting, and production operations.
• Experience troubleshooting complex production environments and performing root-cause analysis.
• Understanding of data ingestion, transformation, orchestration, data quality, performance optimization, and reliability.
• Ability to quickly understand existing systems, codebases, pipelines, configurations, and client-specific tools and workflows.
• Strong communication and collaboration skills across engineering and business teams.

• Experience supporting large-scale, business-critical data platforms with demanding availability, reliability, and data-freshness requirements.
• App Engine and other GCP-hosted applications.
• Cloud Run, GKE, or other GCP application services.
• CI/CD and Dev Ops practices on GCP.
• Infrastructure as Code, particularly Terraform.
• Data observability and automated data-quality monitoring.
• Supporting AI/ML, GenAI, or LLM-based applications running on GCP, including Vertex AI.
• Ability to quickly learn and operate within enterprise-specific analytics, engineering, and operational tooling.

The expected salary range for this position is $80,000 - $150,000 CAD per year, based on experience, skills, and qualifications.