Chief Graph Computing Architect

3 days ago

Kannur, Kerala, India beBeeExpert Full time ₹ 1,50,00,000 - ₹ 2,00,00,000

Entity Resolution and Graph Analytics Expert

The successful candidate will lead the development of our Entity Resolution and Network Generation services. This involves architecting and implementing distributed graph computing solutions that process billions of entities and relationships.

The Platform Overview

Our cloud-native platform leverages a microservices architecture with Kubernetes orchestration, Apache Spark for distributed processing, Elasticsearch for real-time search and fuzzy matching, Scala as the primary development language, data mesh principles with API-first design, and scalable storage systems.

Key Responsibilities

  • Design and implement distributed entity resolution algorithms capable of processing billions of records, leveraging expertise in probabilistic record linkage and similarity measures.
  • Develop blocking strategies optimized for Spark at scale, and create ML-enhanced matching with explainable AI for match decisions.
  • Implement incremental resolution supporting real-time and batch modes, and design APIs for entity lookup with sub-100ms latency requirements.
  • Architect distributed graph generation pipelines using GraphX/GraphFrames, implement graph analytics algorithms (PageRank, community detection, centrality measures), and design storage strategies for multi-billion edge graphs in Parquet/distributed file systems.
  • Build temporal graph support for time-evolving networks, create high-performance graph serving APIs with complex query capabilities, and optimize graph partitioning to minimize shuffle and maximize locality.

Technical Requirements

  • Expert-level Scala programming skills, with 7+ years of experience in distributed computing and big data systems, and 5+ years specifically in entity resolution and graph analytics at scale.
  • Deep experience with Apache Spark, including custom optimizations, production experience with Elasticsearch for search and matching, and proven track record building systems processing billions of entities/edges.
  • Strong understanding of blocking algorithms and their trade-offs, experience with graph algorithms and their distributed implementations, knowledge of graph storage formats and query optimization, and understanding of ML applications in entity resolution.

Desirable Qualifications

  • PhD in Computer Science or related field with focus on graphs/entity resolution, contributions to open-source projects (especially Spark, GraphX, Elasticsearch), experience with graph databases (Neo4j, Neptune, JanusGraph) or equivalent, and publications or conference talks on entity resolution or graph analytics.
  • Experience with real-time stream processing (Kafka, Spark Streaming), knowledge of graph neural networks and embedding techniques, and proficiency in multiple programming languages, including Scala, Python, and Java.

Impact You Will Make

  • Define the architecture for entity resolution serving multiple business domains, build the graph intelligence layer powering advanced analytics and ML, create systems that will process billions of entities with millisecond latencies, establish best practices for graph computing in our organization, and mentor other engineers on distributed graph algorithms.

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