Senior Data Engineer with Google Cloud Spanner and Graph, Graph Platform
- Remote.Bulgaria
- Remote.Georgia
- Remote.Kazakhstan
- Remote.Poland
- Алмати
- Астана
- Белград
- Варна
- Варшава
- Вроцлав
- Днепър
- Ереван
- Киев
- Клуж-Напока
- Краков
- Ларнака
- Лвов
- Лодз
- Люблин
- Одеса
- Рига
- София
- Тбилиси
- Харков
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Project overview
Position overview
Technology stack
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
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