GCP Spanner Data Engineer

Job Overview

Looking for a seasoned GCP Data Engineer to design, build, and maintain large-scale data pipelines and infrastructure on Google Cloud Platform. You will work closely with data architects, analysts, and product teams to deliver reliable, performant, and cost-efficient data solutions.

• Design and implement scalable data pipelines using GCP‑native services (Dataflow, Dataproc, Pub/Sub, Cloud Composer/Airflow)
• Architect and optimize Big Query datasets, tables, and queries for analytical workloads at scale
• Design and manage Cloud Spanner schemas for globally distributed, strongly consistent transactional data
• Build and maintain data models, transformations, and orchestration workflows using Cloud Workflows and related tools
• Develop backend data services and ETL/ELT scripts in Python
• Integrate and manage Firestore for real‑time, document‑oriented data use cases
• Design and manage the GraphQL schema
• Build highly optimized resolver functions that bridge the GraphQL schema directly to data warehouses
• Implement GraphQL Subscriptions to stream live data, event changes, or real‑time metrics using message brokers like Apache Kafka
• Implement data governance, lineage, and quality frameworks using tools like Dataplex or Data Catalog
• Collaborate on infrastructure‑as‑code using Terraform for GCP resource provisioning
• Monitor pipeline health, optimize costs, and troubleshoot production issues
• Mentor junior engineers and contribute to architectural decisions and best practices

• Bachelor’s degree in Computer Science, Engineering, or a related field; OR equivalent combination of education and relevant experience.
• 10+ years of overall experience in data engineering or a related field
• 5+ years of hands‑on experience on Google Cloud Platform
• Strong proficiency in Python for data processing, automation, and pipeline development
• Deep expertise in Big Query — schema design, partitioning, clustering, query optimization, cost governance
• Production experience with Cloud Spanner — schema design, interleaving, transaction patterns, and performance tuning
• Solid understanding of GCP data services:
  Dataflow, Pub/Sub, Cloud Storage, Dataproc, Cloud Composer
• Experience with Cloud Workflows for serverless orchestration
• Hands‑on experience with Firestore (Native mode preferred) for No
  
  SQL/document storage patterns
• Strong SQL skills and understanding of data warehousing concepts
• Experience with CI/CD pipelines (Cloud Build, Git Hub Actions) and version control (Git)

• Experience with dbt for transformation layer on Big Query
• Familiarity with streaming architectures (exactly‑once semantics, late data handling)
• Knowledge of data mesh or data lakehouse patterns
• Exposure to Vertex AI or ML pipelines for MLOps workflows
• GCP Professional Data Engineer certification