Our group: The Bloomberg Query Language (BQL) standardizes how data is accessed across the company. The BQL and Semantic Platform organization develops and maintains BQL along with the semantic infrastructure that models and manages the data and metadata behind it. Our goal is to build the Bloomberg Knowledge Graph, which will power the next generation of Bloomberg applications. To achieve this goal, we provide tools for model creation and metadata management, the infrastructure for ingesting, validating, inferring, and storing the well-structured data, as well as providing the APIs that enable seamless access to it. Our team: As part of a large initiative to organize our data using Semantic Technologies at Bloomberg, we are developing a triplestore, a type of graph database. The general idea behind Semantic Technologies is that all data can be modeled, serialized, and (logically) linked into a large Knowledge Graph, which can then be queried to answer questions like "How is my portfolio affected by the recent election results in Turkey?". The database will be used to store our semantic models and select datasets that are used by various engineering teams within the company. The team will be responsible for ensuring that information is correctly stored and made queryable on a highly available system with optimal runtime characteristics and reliability expected from any enterprise grade database. Our tech stack: RDF4J, LMDB/RocksDB, Postgres, Java, Python, SPARQL, SHACL, Inferencers, R2RML, RDF