Data Engineer

Data Pipeline Development: Design, build, and optimize robust and scalable data pipelines to ingest, transform, and load data from various sources into our data warehouse and knowledge graphs. 

Cloud Data Stack Expertise: Implement and manage data solutions using Google Cloud Platform (GCP) services such as BigQuery, Dataflow, Pub/Sub, Cloud Storage, Spanner and Dataproc and Azure Cloud Services  

Knowledge Graph Engineering: Develop and maintain data models, ingest data, and create efficient queries within Neo4j and/or Stardog. Leverage your expertise to build and expand our enterprise knowledge graph. 

Data Quality & Governance: Implement best practices for data quality, data validation, and data governance, ensuring data accuracy, consistency, and reliability. 

Performance Optimization: Continuously monitor and optimize the performance of data pipelines and database queries, identifying and resolving bottlenecks. 

Collaboration: Work closely with data scientists, analysts, and other engineering teams to understand data requirements and deliver effective data solutions. 

• Documentation: Create and maintain comprehensive documentation for data pipelines, data models, and knowledge graph schemas.