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Job Description & How to Apply Below
Embedded in an AI / digital use‑case delivery squad
- Embedded in an AI / digital use‑case delivery squad
- Reports to the Consulting Delivery Lead
- Works closely with product managers, data scientists, data engineers, and solution architects
- Individual Contributor (IC)
- Embedded in an AI / digital use‑case delivery squad
- Reports to the Consulting Delivery Lead
- Works closely with product managers, data scientists, data engineers, and solution architects
We are looking for an Upstream Engineering Subject Matter Expert with 5-10 years of Oil & Gas experience across petroleum, reservoir, and/or production engineering, who can act as the functional bridge between upstream business teams and technical delivery teams.
The candidate does not need to code, but must be comfortable translating upstream engineering requirements into technical inputs, validating analytics and AI outputs, and working closely with technical teams throughout design, build, and deployment.
This role supports AI-enabled and advanced analytics use cases across the upstream value chain (subsurface through production and surface facilities), and success depends on domain credibility, structured problem-solving, and strong documentation skills.
- English:
Mandatory - Arabic:
Optional - Certifications:
Not required beyond core engineering experience
Experience
- 5-10 years of upstream engineering experience in Oil & Gas
- Background in one or more of the following disciplines:
- Reservoir Engineering
- Production Engineering
- Petroleum Engineering
- Experience may come from operating companies, service companies, or integrated project teams
- Exposure to digital, analytics, or optimization initiatives is preferred but not mandatory
- Can clearly explain how upstream engineering workflows operate in practice (not just theory)
- Understand operational pain points, uncertainties, and data quality challenges across subsurface and production domains
- Can articulate trade-offs between reservoir performance, well design, production efficiency, and operating constraints
- Are comfortable writing structured documentation such as:
- Use-case descriptions
- Functional requirements
- Assumptions and constraints
- Can engage credibly with subsurface teams, production operations, and technical leadership
The Ideal Candidate Will Be Able To
- Work with data science and engineering teams to define:
- Features
- Inputs
- Engineering constraints
- Review and validate analytics, optimization, and AI outputs from a domain realism perspective
- Understand basic analytics concepts such as:
- Metrics and KPIs
- Data quality and completeness
- Time-series and classification outputs
- Participate in user acceptance testing (UAT) and assess whether insights make sense operationally
- Translate upstream engineer feedback into clear requirements or backlog items for developers
Note:
They do not need to be a data scientist, ML engineer, or software developer.
- Business Requirements & Translation
- Lead discovery sessions with upstream engineering and operations stakeholders
- Capture and document engineering use cases, decision workflows, and success criteria
- Translate business and engineering needs into clear functional requirements and acceptance criteria
- Use Case Build Support
- Provide domain input into upstream AI/analytics use cases such as:
- Reservoir performance and forecasting analytics
- Production optimization and surveillance
- Well performance diagnostics and root cause analysis
- Decline analysis and production anomaly detection
- Field development and planning decision support
- Support definition of engineering variables, calculations, and data classifications
- Validate model outputs, dashboards, and insights against engineering expectations
- Stakeholder Engagement
- Act as a trusted interface between:
- Subsurface and production teams
- Digital product and data teams
- Support pilot deployments and phased rollouts
- Gather structured feedback from engineers and operators to refine solutions
- Governance & Quality
- Support definition of data quality standards and engineering assumptions
- Ensure solutions align with…
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