Snr Data Architect/Data modeler - Global Channel Management
Snr Data Architect/Data modeler needs 3 years of experience in data design/architecture and strong willingness to continue learning
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 AccountingRecent 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 termsExperience 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 maintainedDrive strategies/approaches and principles for data management (including master/reference data and identification of key data domains, data governance framework, data integration, etc.)