Knowledge Graph Engineer

18 days ago

Pittsburgh, PA, USASenior / Staff+

H1B sponsor

Responsibilities

Perform entity resolution and map source data to IDS entities, relationships, and attributes for enterprise knowledge graph integration.
Design and build scalable batch, streaming, and near-real-time pipelines for internal and external data sources.
Transform APIs, flat files, streaming data, and unstructured content into standardized datasets aligned to IDS models.
Develop reusable frameworks for identifier, symbology, unit, hierarchy, and event-data normalization.
Implement data quality, schema validation, anomaly detection, lineage, provenance, and traceability controls.
Support multi-vendor ingestion, comparison, reconciliation, source prioritization, and coverage and quality analytics.
Build modular, cloud-native pipelines optimized for scalability, performance, reliability, and cost efficiency.
Collaborate with platform, product, data, and ontology teams to deliver production-ready solutions and downstream APIs and data products.

Bachelor's degree in a related discipline or equivalent work experience.
Typically 8-12 years of experience, with at least 4 years focused on data analysis and business intelligence preferred.
Expertise with RDF, OWL, SHACL, SPARQL, LPG, Cypher, and GQL, including round-tripping between graph models without semantic drift.
Experience building ontology-based knowledge graphs and evaluating graph persistence and virtualization options.
Experience with graph modularization, versioning, temporality, data entitlements, licensing, and usage tracking.
Understanding of AI-to-knowledge-graph integration patterns, including MCP, Graph RAG, identity resolution, entity and relationship extraction, hybrid KG/vector retrieval, text-to-query, and agentic workflows.
Experience with production-grade data engineering and high-volume resilient pipelines using Python, Spark, SQL, ETL/ELT frameworks, and orchestration tools.
Experience designing transformation and normalization layers, schema evolution, backward compatibility, performance tuning, cost optimization, monitoring, logging, and lineage frameworks.
Expertise with Snowflake, AWS, Databricks, lakehouse architectures, and API-based data integration.
Financial dataset experience covering market data, pricing, reference data, portfolio holdings, transactions, or corporate actions; familiarity with Bloomberg, ICE, or MSCI is preferred.
Advanced degree, preferably involving statistics or statistical analysis, is preferred.

10,000+ employees