Reference Data Sr Associate Engineer

Roles & Responsibilities:

• Work with Reference Data Product Owner, external resources and other engineers as part of the product team
• Develop and maintain semantically appropriate concepts
• Identify and address conceptual gaps in both content and taxonomy
• Maintain ontology source vocabularies for new or edited codes
• Support product teams to help them leverage taxonomic solutions
• Analyze the data from public/internal datasets.
• Develop a Data Model/schema for taxonomy.
• Create a taxonomy in Semaphore Ontology Editor.
• Perform Bulk-import data templates into Semaphore to add/update terms in taxonomies.
• Prepare SPARQL queries to generate adhoc reports.
• Perform Gap Analysis on current and updated data
• Maintain taxonomies in Semaphore through Change Management process.
• Develop and optimize automated data ingestion / pipelines through Python/PySpark when APIs are available
• Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs
• Identify and resolve complex data-related challenges
• Participate in sprint planning meetings and provide estimations on technical implementation.

Basic Qualifications and Experience:

• Any degree with 5 - 9 years of experience in Business, Engineering, IT or related field

Functional Skills:

Must-Have Skills:

• Knowledge of controlled vocabularies, classification, ontology and taxonomy
• Experience in ontology development using

  Progress

  Semaphore

  , or a similar tool like Pool Party etc
• Hands on experience writing

  SPARQL queries on graph data

• Excellent problem-solving skills and the ability to work with large, complex datasets
• Strong understanding of data modeling, data warehousing, and data integration concepts

Good-to-Have Skills:

• Hands on experience writing SQL using any RDBMS (Redshift, Postgres, MySQL, Teradata, Oracle, etc. ).
• Experience using cloud services such as AWS or Azure or GCP
• Experience working in Product Teams environment
• Knowledge of Python/R, Databricks, cloud data platforms
• Knowledge of NLP (Natural Language Processing) and AI (Artificial Intelligence) for extracting and standardizing controlled vocabularies.
• Strong understanding of data governance frameworks, tools, and best practices

Professional Certifications:

• Databricks Certificate preferred , Progress Semaphore
• SAFe Practitioner Certificate preferred
• Any Data Analysis certification (SQL, Python)
• Any cloud certification (AWS or AZURE)

Soft Skills:

• Strong analytical abilities to assess and improve master data processes and solutions.
• Excellent verbal and written communication skills, with the ability to convey complex data concepts clearly to technical and non-technical stakeholders.
• Effective problem-solving skills to address data-related issues and implement scalable solutions.
• Ability to work effectively with global, virtual teams