BNY
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We are seeking a Knowledge Graph Engineer to join our Data Innovation team.
We are seeking a Knowledge Graph Engineer to join our Data Innovation team. In this role, you will design and build the data pipelines and graph-enabled data structures that power our Investment Data Standard (IDS) and enterprise knowledge graph, enabling a unified, high-quality data ecosystem that supports analytics, AI, and client-facing solutions.
You will work closely with the Ontology and Knowledge Architecture lead and collaborate across platform, product, and data teams to deliver scalable, production-ready solutions aligned with our broader data transformation strategy. This role is located in New York, NY, Boston, MA, Pittsburgh, PA or Lake Mary, FL.
Partner with the Ontology and Knowledge Architecture team to perform entity resolution, map source data to IDS entities, relationships, and attributes, and integrate it into the enterprise knowledge graph.
Design and build scalable pipelines to ingest and process data from internal platforms and external vendors across batch, streaming, and near-real-time patterns.
Transform diverse data formats, including APIs, flat files, streaming data, and unstructured content, into clean, standardized datasets aligned to IDS entity models.
Develop reusable frameworks to normalize identifiers, symbology, units, hierarchies, and event data such as corporate actions and transactions.
Implement robust data quality controls, including completeness, accuracy, consistency, schema validation, anomaly detection, lineage, provenance, and traceability from source systems through IDS to downstream products.
Support multi-vendor data ingestion, comparison, and reconciliation, including source prioritization, hierarchy logic, and coverage and quality analytics.
Build modular, reusable, cloud-native pipelines optimized for scalability, performance, reliability, and cost efficiency.
Collaborate cross-functionally to translate business and data requirements into production-ready solutions and support downstream distribution through APIs, data products, and client platforms.
Bachelor's degree in a related discipline or equivalent work experience required. An advanced degree with a preference in statistics/statistical analysis is preferred.
Typically, 8-12 years of experience, with at least 4years’ experience with a strong focus on data analysis and business intelligence is preferred.
Expert command of both RDF/OWL/SHACL/SPARQL and LPG/Cypher/GQL. Bonus points if you can round-trip data between the two while avoiding semantic drift.
Experience in building ontology-based knowledge graphs. Understand the options for graph data persistence and virtualization and be able to elucidate the tradeoffs. Experience beyond R2RML (NoSQL, API, unstructured data, etc.) is a plus.
Strong perspective on graph modularization, versioning, and temporality.
Solid understanding of AI-to-KG integration patterns (MCP, Graph RAG, AI-assisted Identity resolution and Entity/Relationship extraction, hybrid KG/Vector retrieval, text-to-query, agentic workflows).
Familiarity with the current knowledge graph technology landscape, including vendor solutions and open-source alternatives; broader awareness of adjacent technologies such as data catalogs and semantic layers is a plus.
Understanding data entitlements, licensing, and usage tracking.
Experience in data engineering, building and scaling production-grade data pipelines (Python, Spark, and SQL), with strong understanding of ETL/ELT frameworks and orchestration tools.
Proven ability to design and operate high-volume, resilient pipelines across batch, streaming, and distributed environments.
Experience designing data transformation and normalization layers, including schema evolution and backward compatibility.
Expertise with modern data platforms (e.g., Snowflake, AWS, Databricks), lakehouse architectures, and API-based data integration.
Strong capabilities in performance tuning, cost optimization, and implementing data quality, monitoring, logging, and lineage frameworks.
Domain experience with financial datasets (market data, pricing, reference data, portfolio holdings, transactions, corporate actions) and familiarity with key vendors (e.g., Bloomberg, ICE, MSCI).
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