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 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 UDO's 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.)

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