Purpose & Accountabilities :

TAO SSW (Subsurface and Wells) Team is tasked with delivering tangible value to various business units within Shell through Analytics driven decision making.

1. Subsurface and wells (Technical Reporting) team delivering advanced analytics projects for upstream businesses within Shell

2. Incumbent is responsible for supporting the development of technical models for projects collaborating with different business stakeholders & other partners and working across a range of technologies and tools.

3. The ideal candidate should have good background in data warehousing concepts and Spotfire/ Microsoft Power BI reports development

4. Enhances organization reputation by accepting ownership for accomplishing new and different requests; exploring opportunities to add value to job accomplishments

Skills & Requirements:

1. Degree/master’s in information management (degree in relevant Oil & Gas discipline an advantage). A keen desire and ability to learn new things and assimilate knowledge will be essential.

2. 2-3 years of experience building analytical models desirable. Experience in Spotfire/Microsoft Power BI professional for loading data from databases, creating visualization and filters.

3. Ability to transform business level requirements to efficient dashboards and visualizations.

4. Awareness of Spotfire/Microsoft Power BI architecture and different servers.

5. Experience in creating customized visualizations, custom expressions, inserting rows/columns, Data on demand, property controls, text areas, properties of different charts.

6. Should have knowledge on Data Warehousing. Good to have knowled in scripting language like iron python.

7. Proficiency in advanced features like multiple filtering scheme, custom expression, multiple marking, multiple data table and relation between multiple data table, property control etc

8. Work closely with QA, Data architects, to develop dashboards and understand user behaviors, log analysis, and known software bugs.

9.Troubleshoot visualizations in increasing the load efficiency in dashboards

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