TigerGraph Engineer

SoftServe is a global digital solutions company headquartered in Austin, Texas, founded in 1993. The Big Data & Analytics team builds end-to-end data & analytics solutions for clients across healthcare, finance, manufacturing, retail, and energy industries. The company holds top-level partnerships with major cloud providers and collaborates with technology leaders such as AWS, GCP, Microsoft, Databricks, Snowflake, and Confluent.

• 5+ years of hands-on experience designing and building scalable data pipelines
• Proficient in working with Graph Databases and understanding graph structures, relationships, and performance considerations
• Skilled in Python/PySpark and SQL for data transformation, integration, and logic building
• Comfortable with web scraping and working with diverse data sources
• Effective communication skills at an upper-intermediate English level or higher
• Experienced or familiar with TigerGraph (preferably) or Neo4j, especially in supply chain or complex relationship modeling (a strong plus)

• Be part of a project combining graph analytics, data engineering, and AI to deliver real business impact
• Build and support a fraud investigation tool based on graph and network analysis
• Model relationships between entities (e.g., devices, IPs) to uncover fraud signals and suspicious patterns
• Implement and optimize graph-based data pipelines and analytics solutions
• Work with TigerGraph algorithms to detect clusters, anomalies, and hidden connections
• Contribute to an AI layer that explains detected fraud patterns in a clear and actionable way
• Collaborate with analysts and stakeholders to improve fraud detection and investigation processes
• Transform complex graph insights into clear outputs for end users and investigators

SoftServe is an Equal Opportunity Employer. All qualified applicants will receive consideration regardless of race, color, religion, age, sex, nationality, disability, sexual orientation, gender identity and expression, veteran status, and other protected characteristics under applicable law.