We are seeking a Reference Data Management Senior Analyst to join the Enterprise Data Management organization. In this vital role, you will be responsible for managing and promoting the use of reference data, partnering with business Subject Matter Experts on the creation of vocabularies and taxonomies, and developing analytic solutions using semantic technologies. The ideal candidate will have a strong understanding of data modeling, ontology development, and experience with large, complex datasets.
Roles & Responsibilities
- Ontology & Taxonomy Development: Work with the Reference Data Product Owner to develop and maintain semantically appropriate concepts. You will create taxonomies and ontology source vocabularies using tools like Semaphore Ontology Editor and perform bulk-import data updates.
- Data Analysis & Solutions: Analyze data from public and internal datasets, develop a data model/schema for taxonomies, and perform gap analysis on current and updated data. You will also develop analytic solutions using semantic technologies and write SPARQL queries for ad-hoc reports.
- Automation & Integration: Develop and optimize automated data ingestion pipelines using Python/PySpark when APIs are available. You will collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs.
- Support & Maintenance: Support product teams in leveraging taxonomic solutions and maintain taxonomies in Semaphore through the Change Management process. You will participate in sprint planning meetings and provide estimations on technical implementation.
- Problem-Solving: Identify and resolve complex data-related challenges, ensuring data integrity and accuracy.
Technical Skills
- Strong knowledge of controlled vocabularies, classification, ontology, and taxonomy.
- Experience in ontology development using Semaphore or a similar tool.
- Hands-on experience writing SPARQL queries on graph data.
- Excellent problem-solving skills and the ability to work with large, complex datasets.
- Understanding of data modeling, data warehousing, and data integration concepts.
- Experience with cloud services like AWS, Azure, or GCP is a plus.
- Knowledge of Python/R, Databricks, and cloud data platforms is a plus.
- Databricks Certificate, SAFe Practitioner Certificate, or a Data Analysis certification is preferred.
- Knowledge of NLP (Natural Language Processing) and AI (Artificial Intelligence) for extracting and standardizing controlled vocabularies is a plus.
Qualifications
- A Master's degree with relevant experience, a Bachelor's degree with extensive experience, or a Diploma with a substantial background in Business, Engineering, IT, or a related field.
Soft Skills
- Analytical Abilities: Strong analytical abilities to assess and improve master data processes and solutions.
- Communication: Excellent verbal and written communication skills, with the ability to convey complex data concepts to both technical and non-technical stakeholders.
- Problem-Solving: Effective problem-solving skills to address data-related issues and implement scalable solutions.
- Collaboration: The ability to work effectively with global, virtual teams.
