Data Engineer

We are looking for a data engineer. In this role, you will get to work alongside various experts in product and engineering, contributing to the development and optimization of our data infrastructure. This is an exciting opportunity for engineers eager to delve into the realm of modern graph and vector databases, as well as working on streaming data pipelines, leveraging cutting-edge technologies to drive insights and innovation within our organization. If you're passionate about shaping the future of data architecture and thrive in a collaborative environment, we invite you to join our dynamic team.

Example Responsibilities:

• Develop real-time graph data pipelines that capture and process relevant data, integrating vector databases for efficient similarity searches to uncover patterns indicative of fraudulent activities.
• Deploy the integrated solution to production, ensuring a robust and scalable infrastructure. Conduct benchmarking and performance optimization for both vector and graph databases to meet the demands of real-time fraud detection.
• Design and implement a cohesive system where vector and graph infrastructure complement each other seamlessly, combining their strengths to enhance fraud detection capabilities.
• Collaborate with cross-functional teams to identify specific use cases where vector and graph databases can work in tandem, devising data models that optimize the strengths of both technologies for effective fraud detection.
• Collaborate with the operations team to define and implement efficient maintenance and update strategies for both vector and graph databases, ensuring seamless operation in a dynamic production environment.

Experience and Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.
• Proficient in Python, Java, or Scala for developing and maintaining data engineering pipelines, with expertise in Apache Spark, Flink, and containerization (Docker, Kubernetes).
• Experienced in cloud platforms (AWS, Azure, Google Cloud) and skilled in working with various databases and data warehouses for efficient data processing and storage.
• Familiar with operations tools such as Terraform, Jenkins, and CloudFormation for automating infrastructure provisioning, deployment, and maintenance in production environments.

Great to have Experience and Qualifications:

• Experience in designing, implementing, and optimizing real-time graph data pipelines for fraud detection, with a proven track record of deploying solutions to production environments.
• Solid understanding of embedding techniques, graph neural networks, and their applications in solving real-world problems.
• Proficiency in graph theory, graph algorithms, and graph databases (e.g., Neo4j, Amazon Neptune, TigerGraph, …), coupled with extensive knowledge of vector databases (OpenSearch, Milvus, …).
• PhD in Computer Science or a related field with a focus on graph-based machine learning.
• Experience working with large-scale graph datasets and optimizing performance for computational efficiency.
• Contributions to relevant open-source projects.
• Strong analytical and problem-solving skills with a keen eye for detail in optimizing algorithms for performance and scalability.

Key Characteristics and Attitudes:

In a recent global survey these attributes were valued by Jumios in all locations and functions - we firmly believe in hiring for attitude as well as skill.

• Friendly and supportive
• Adaptable and flexible
• Articulate and persuasive
• High IQ and EQ
• Curious and coachable
• Commercially Aware
• Resilient and tenacious
• Big picture and the detail