Senior Data Engineer with Google Cloud Spanner and Graph, Graph Platform

About the Position

We are looking for a Senior Data Engineer with strong experience in Google Cloud Spanner and graph technologies to contribute to a high performance data platform. You will work at the intersection of relational, vector, and graph data models, helping to design and optimize a unified data layer that supports advanced analytics and real time retrieval.

About the Project

This project focuses on building a unified Spanner based data platform that combines relational storage, graph modeling, and vector search to enable hybrid data access patterns. The solution supports complex graph traversals and near real time synchronization across multiple data representations.

Responsibilities

• Design and implement Cloud Spanner schemas including interleaved table structures to optimize performance and data locality
• Collaborate with the database and architecture teams to define unified relational and graph data models
• Develop and optimize advanced SQL and ISO GQL queries to support efficient graph traversals and hybrid access patterns
• Build and maintain CDC pipelines to synchronize relational, graph, and vector data in near real time
• Design and implement ETL and ELT processes to support data ingestion and transformation
• Optimize database performance through query tuning, indexing strategies, and workload optimization
• Implement graph modeling approaches to represent complex relationships and enable advanced querying
• Support vector search capabilities integrated with graph and relational data layers
• Ensure data consistency, correctness, and synchronization across all data representations
• Collaborate with cross functional teams to deliver scalable, reliable, and observable data pipelines

Requirements

• Strong data engineering background with hands on experience in building data platforms
• Experience working with Google Cloud Spanner in production environments
• Advanced SQL skills including query optimization and performance tuning
• Experience designing and implementing CDC pipelines and real time data synchronization
• Hands on experience with ETL and ELT processes and data pipeline architecture
• Proficiency in Python for data processing and pipeline development
• Experience with graph modeling and familiarity with graph query languages such as GQL
• Understanding of distributed data systems and scalable architecture patterns
• Familiarity with Google Cloud Platform services such as BigQuery, Pub Sub, and Dataflow
• Knowledge of data governance concepts including data quality, lineage, and consistency
• Understanding of data security practices including IAM and encryption standards

Nice to Have

• Experience with vector search technologies and embedding based retrieval
• Familiarity with Apache Beam for distributed data processing
• Experience working with hybrid architectures combining relational, graph, and vector data
• Exposure to AI driven data platforms or machine learning pipelines
• Experience with observability tools for monitoring data pipelines and system performance

Technologies

Google Cloud Platform, Cloud Spanner, BigQuery, Pub Sub, Dataflow, SQL, ISO GQL, Python, Apache Beam, CDC pipelines, ETL and ELT frameworks, graph databases, vector search technologies, IAM, encryption