Graph Database Technical Architect

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.