Data Engineer

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Job Description:

Job Description

We're seeking a talented Data Engineer with hands-on experience in the Google Cloud data ecosystem and a proven track record of working with Vector Database, Knowledge Graphs like Neo4j and StarDog. You'll be instrumental in designing, building, and maintaining our data infrastructure and pipelines, enabling critical insights and supporting data-driven initiatives across the organization.

Responsibilities

• 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.

Required Qualifications

Education:

Bachelor's degree in computer science, Engineering, or a related quantitative field.

Experience:

• 3+ years of professional experience as a Data Engineer or in a similar role.

• Strong hands-on experience with Google Cloud Platform (GCP) data services, including BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Composer (Apache Airflow).

• Demonstrable experience working with Knowledge Graph technologies, specifically Neo4j and/or Stardog. This includes data modeling, query language proficiency (Cypher for Neo4j, SPARQL for Stardog), and integration.
• Proficiency in Python for data manipulation and pipeline orchestration.
• Experience with SQL and data warehousing concepts.
• Familiarity with data modeling techniques for both relational and graph databases.
• Experience with version control systems (e.g., Git).

Preferred Qualifications

• Experience with other GCP services like Data Catalog, Looker, or Vertex AI.
• Knowledge of streaming data technologies (e.g., Kafka, Google Cloud Dataflow streaming).
• Experience with other graph databases or semantic web technologies.
• Familiarity with data governance tools and principles.

Certifications in Google Cloud Platform data engineering.

Employee Type:

Permanent

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