Key Responsibilities: AWS knowledge, graph technology knowledge, supply chain background Collaborate with cross-functional teams to understand data requirements and design appropriate graph and time series database schemas. Implement and configure graph and time series databases to meet specific application needs. Ensure data integrity, consistency, and security across databases. Query Optimization and Performance Tuning: Analyze and optimize query performance for graph traversal and time series data retrieval. Identify and resolve bottlenecks to improve overall database performance. Monitor and fine-tune database parameters to enhance system efficiency. Develop and maintain data models for graph and time series data structures. Map complex relationships and hierarchies for efficient graph traversals. Define retention policies and storage strategies for time series data. Integrate graph and time series databases with existing systems and applications. Implement data pipelines for seamless data ingestion and synchronization. Work with ETL processes to ensure timely and accurate data updates. Implement security measures to protect sensitive data stored in the graph and time series databases. Ensure compliance with relevant data protection regulations and industry standards. Monitor database health and diagnose and resolve issues as they arise. Required Qualifications: 15+ experience with Proven development experience within Graph database technologies (e.g., Neo4j, Amazon Neptune), Time Series databases (e.g., InfluxDB, TimescaleDB, AWS Timestream), NOSQL (eg., AWS DynamoDB, MongoDB) and In-memory(eg., AWS MemoryDB for Redis) Graph Query Languages like openCypher, Gremlim, or SPARQL Proficiency in Python programming Solid knowledge of graph database concepts, including graph data modeling, traversal algorithms, etc. Experience in GraphDB tuning techniques, eg: partitioning and sharding, creating and using indexes, Query profiling, Configure the page cache, Configuration parameters, and memory tuning, garbage collection, etc. Implement data ingestion pipelines to efficiently capture and store real-time and historical time series data from various sources. Design and develop time series database architectures that efficiently manage high-frequency, chronological data. Proficiency in database design, query optimization, and performance tuning. Strong understanding of data modeling principles for graph structures and time-based data. Familiarity with real-time data processing frameworks (e.g., Kafka, Apache Flink) is a plus. Excellent problem-solving and troubleshooting skills. Strong communication and collaboration abilities. Ability to work independently and manage multiple tasks simultaneously.