Snr Data Architect/Data Modeler

Snr Data Architect/Data modeler requires: Master's or Bachelor's degree in business, computer science, engineering, systems analysis or a related field Minimum 3 years of experience in data design/architecture and strong willingness to continue learning Minimum 3 years of experience in Upstream Oil and Gas Production Optimization, Production Volumes, and Production Revenue Accounting Recent experience developing reference data architecture, data modeling (conceptual, logical,physical,and Type 2 Dimensional Modelling), data profiling, data quality analysis, building business data glossaries and data catalogs Knowledge regarding data governance and master/reference data management programs Experience using Snowflake, SQL query language and E/R Studio data modeling too Able to design solutions around Role-Based Access (RBA) Preferred; Experience and knowledge of Upstream Oil & Gas Production Optimization, Production Volumes, and Production Revenue Accounting - business processes and business terms Experience with tools such as TAMR, Collibra, and ER Studio Understanding of large data store technologies (Data Lakes, Data Warehouse, Data Hubs, etc.) Specifically Snowflake Experience with Type 2 Dimensional Modelling Knowledge of JSON, Python, GIT; understanding of API concepts and integration architecture Knowledge of TOGAF Framework and DAMA DMBoK v.2 desirable Knowledge and experience working with Role-based access Snr Data Architect/Data modeler duties: Provide data architectural and modeling support, guidance, and mentorship to data engineering product teams to ensure they can successfully deliver, support, and where applicable standardize data products Partner with IT to ensure Upstream Data Foundation Data Platforms and related tooling satisfies UDOs business needs Work with Data Governance teams to ensure business glossaries, data dictionaries and data catalogs are created and maintained Drive strategies/approaches and principles for data management (including master/reference data and identification of key data domains, data governance framework, data integration, etc.)