Design and build scalable data platforms and pipelines to process large-scale datasets across distributed systems. Develop data processing and analytics solutions to derive insights from complex and high-volume data. Build and maintain batch and streaming pipelines using modern data technologies (e.g., Spark, Kafka). Design and implement graph-based data models, enabling efficient representation of relationships across entities. Develop graph traversal and relationship analysis logic to support advanced querying and insights (e.g., multi-hop analysis, entity linking). Collaborate with cross-functional teams to translate business requirements into scalable technical solutions. Ensure data quality, reliability, and performance in production systems. Drive end-to-end ownership from system design and implementation to deployment and monitoring. * 7+ years of experience in software engineering, data engineering, or related roles. Strong fundamentals in data structures, distributed systems, and data modeling (including graph modeling concepts). Hands-on experience with big data technologies (e.g., Spark, Kafka, distributed storage systems). Proficiency in Python and SQL. Experience building scalable batch and/or streaming pipelines. Familiarity with graph processing frameworks or graph databases (e.g., GraphFrames, Neo4j, TigerGraph, NetworkX). Understanding of graph traversal techniques (e.g., BFS/DFS, multi-hop queries, relationship aggregation). Experience working with large datasets and optimizing performance (joins, partitioning, skew handling). Experience with cloud platforms (Azure/AWS/GCP) is a plus.
