Data Catalog Stewardship Analyst (Neo4j)

We are looking for a Data Catalog Stewardship professional to support the design, development, and governance of the enterprise data catalog using knowledge graphs, Neo4j, ontology modeling, and Graph RAG capabilities. The role will work closely with data governance, data engineering, data quality, business, risk, compliance, and analytics teams to enhance metadata quality, improve data discoverability, strengthen lineage traceability, and enable AI-ready cataloging across the bank's data ecosystem.

Responsibilities:

Own and manage data catalog assets, including datasets, data elements, business terms, technical metadata, classifications, ownership information, and glossary mappings.

Design and enhance knowledge graph-based catalog structures using Neo4j.

Develop and maintain ontology models for business glossaries, data dictionaries, lineage, data quality, CDE/RDE classification, and data ownership.

Support Graph RAG implementations for metadata discovery, semantic search, and AI-powered catalog queries.

Build and maintain relationships between business terms, datasets, source systems, lineage, data quality rules, owners, stewards, and critical data elements.

Partner with business and technology stakeholders to validate metadata definitions, ownership, classifications, and catalog completeness.

Support Critical Data Element (CDE) and Reportable Data Element (RDE) discovery and mapping initiatives.

Contribute to metadata standards, stewardship processes, governance frameworks, and catalog quality controls.

Perform metadata profiling, validation, and analysis using SQL and Python.

Collaborate with data engineering teams to ingest metadata from source systems, data marts, lineage repositories, and data quality platforms.

Ensure catalog content remains accurate, consistent, audit-ready, and aligned with enterprise governance standards.

Prepare stewardship dashboards, governance reports, and catalog progress updates for stakeholders.

Requirements:

2-7 years of relevant experience in data governance, data cataloging, metadata management, knowledge graphs, or related domains.

Strong hands-on experience with Neo4j development and graph databases.

Deep understanding of knowledge graph-based data cataloging concepts.

Strong knowledge of metadata management, business glossaries, data dictionaries, lineage, ownership, and classification frameworks.

Experience in ontology design, semantic data modeling, graph schema design, and entity-relationship modeling.

Understanding of Graph RAG concepts, semantic search, metadata discovery, and LLM-powered catalog querying.

Good understanding of data governance principles, including stewardship, metadata management, data quality, lineage, ownership, and CDE/RDE frameworks.

Hands-on experience with SQL for metadata analysis, profiling, and validation.

Working knowledge of Python for automation, metadata processing, and catalog enrichment.

Strong stakeholder management and communication skills, with the ability to collaborate across business and technology teams.

Preferred Skills:

Experience within banking, financial services, risk, compliance, or analytics domains.

Exposure to enterprise data governance tools such as Collibra, Alation, Informatica, or similar platforms.

Understanding of data privacy, PII classification, regulatory reporting, and audit requirements.

Knowledge of lineage management across source, staging, refined, mart, and consumption layers.

Experience building semantic search, metadata intelligence, or AI-assisted cataloging solutions.

Familiarity with APIs, metadata ingestion frameworks, and graph-based integration patterns.

Technical Skills

Graph and Metadata Technologies: Neo4j, Knowledge Graph Modeling, Graph Schema Design, Entity Mapping, and Relationship Modeling.

Data Governance and Cataloging: Business Glossary, Data Dictionary, Metadata Management, Data Lineage, Data Ownership and Stewardship, and CDE/RDE Classification.

AI and Semantic Search: Graph RAG, semantic search, metadata-driven retrieval, and AI-powered data discovery.

Programming and Databases: Python, SQL.

An Ideal Candidate:

The ideal candidate is a hands-on data governance and cataloging professional with strong expertise in Neo4j, knowledge graphs, and metadata management.

The individual should be comfortable translating business metadata into graph-based catalog structures and driving initiatives that improve data discovery, lineage visibility, governance compliance, and AI-enabled metadata consumption.