Description

You will design and implement scalable graph and time series database architectures to manage complex relationships and high-frequency chronological data.

Responsibilities

• Design graph and time series database schemas and data models to map complex hierarchies and relationships.
• Implement and configure databases to meet application needs while ensuring data integrity, consistency, and security.
• Optimize query performance for graph traversals and time series retrieval through profiling and parameter tuning.
• Build data ingestion pipelines and ETL processes to synchronize real-time and historical data.
• Monitor database health, resolve bottlenecks, and define retention policies for time series storage.

Required Skills

• 15+ years of experience in database development and architecture.
• Proven experience with Graph database technologies such as Neo4j or Amazon Neptune.
• Experience with Time Series databases including InfluxDB, TimescaleDB, or AWS Timestream.
• Proficiency with NoSQL databases like AWS DynamoDB or MongoDB.
• Experience with In-memory databases such as AWS MemoryDB for Redis.
• Expertise in Graph Query Languages including openCypher, Gremlin, or SPARQL.
• Proficiency in Python programming.
• Deep knowledge of GraphDB tuning, including partitioning, sharding, indexing, and memory tuning.
• Strong understanding of graph data modeling and traversal algorithms.

Preferred Skills

• Familiarity with real-time data processing frameworks like Kafka or Apache Flink.

Key Skills

Education

Any Graduate