Data Science In Oil Gas Jobs in Minnesota (NOW HIRING)

The Data Architect provides the technical leadership for Landmark’s AAM/UEM data platform — the layer that ingests, normalizes, and exposes oil & gas data (subsurface, drilling, well planning, production, finance, HSE) for the application layer and, increasingly, for external operators. You will own the data model, the ingestion and harmonization strategy across heterogeneous source systems, and the architectural roadmap for the AI-augmented data workflows that set the platform apart from a generic “ingest everything” data lake. 

This is a deliberately technical, hands-on architecture role. It pairs day-to-day with an internal Data Platform Product Manager, who owns the “what and why” on the product and customer side, while you own the “how” on the data side. The two roles were created together so that customer outcomes and data architecture move in step rather than in opposition. You will work closely with engineers, data scientists, and domain experts, and your decisions about how the platform models and serves data will shape what the product can credibly promise. 

What You Will Do 

Data Architecture and Modelling 

• Own the data model for the platform — the domain model, schema design, and the harmonization strategy that turns heterogeneous source data into a coherent, queryable whole. 
• Set the standards for data quality, cataloguing, lineage, and governance, drawing on recognized frameworks where they help, so the platform is trustworthy enough to build products on and sell. 
• Define the architectural roadmap for the data side and keep it coherent as the platform scales from internal enabler to multi-customer product. 

Ingestion and Integration Across Source Systems 

• Design the ingestion patterns and pipelines that bring data from many engineering and operational systems into a common domain model — robustly, repeatably, and at scale. 
• Lead multi-source data integration: reconciling differing schemas, identifiers, and semantics from upstream systems into something consistent and reusable. Where ontology- or meta-model-driven approaches fit, bring them to bear.
• Define the contracts and APIs through which the application layer and external customers consume platform data, and the clean handoff between the data platform and the products built on it. 

AI-Augmented Data Workflows 

• Own the architecture for AI-augmented data workflows — retrieval-augmented generation, semantic and vector search, and agentic pipelines — that make the platform’s data genuinely useful to engineers and scientists, not just stored. 
• Decide where AI belongs in a data platform for upstream oil & gas, separating real, high-value use cases from buzzwords, and build the patterns the rest of the team will follow. 
• Stay hands-on enough to prototype and validate these workflows yourself, using modern AI-assisted development tools as part of your daily working practice. 

Partnership and Technical Leadership 

• Partner with the Data Platform Product Manager as a true peer — engaging with customer and roadmap trade-offs, pushing back substantively when the data model can’t support a request, and translating technical realities into product decisions. 
• Provide technical leadership and credible review across the data side, and work alongside engineers, data scientists, and domain experts to raise the quality bar. 
• Represent the data platform’s architectural direction in technical and product planning, and help the organization understand what the data layer can and should do. 

• Master’s degree in Computer Science or a closely related STEM discipline. 
• Extensive experience in data architecture — data modelling, schema design, ingestion patterns, data cataloguing, governance, and event-driven data flows. 
• Demonstrated multi-source data integration experience: bringing heterogeneous data from many systems into a coherent, common domain model. 
• Hands-on experience with modern cloud data platforms and tooling (e.g. Azure, Microsoft Fabric, SQL and cloud databases, vector databases, graph databases).
• Practical, shipped experience with AI-augmented data workflows — RAG, semantic and vector search, and agentic pipelines — not just evaluation or demos. 
• Familiarity with oil & gas or energy data domains — subsurface, well, drilling, production, HSE, or finance data. 
• Comfort with AI-assisted development tools (e.g. Claude Code, Copilot, Cursor, MCP-based workflows) in daily work. 

Preferred 

• Direct experience with Norwegian E&P data ecosystems — DISKOS or comparable national/operator repositories — or with major operators such as Equinor. 
• Experience standing up data governance frameworks or a data catalogue (e.g. DAMA-DMBOK or similar), or building out a Head of Data / Chief Data Officer function. 
• Familiarity with ontology- or meta-model-driven integration approaches. 
• Experience with modern agentic AI frameworks (e.g. LangChain, LangGraph, Pydantic-AI) and the patterns for putting them into production. 
• Fluent Norwegian, and the ability to work credibly with both business and engineering stakeholders. 

The data platform is the foundation under AAM/UEM and is increasingly positioned as a sellable product in its own right. This role owns the technical heart of that — with real scope, a peer product partner, and the authority to set the architecture. We build small, high-ownership teams and invest in the quality of every person on them. Engineers and architects here co-author the product strategy alongside product management and domain science: the art of the possible is an engineering contribution, not a function that sits upstream. You will work in a business where software is the product, solving technically demanding problems in a domain where your improvements have visible, measurable impact. We offer competitive compensation including bonus and long-term incentive programs, and the scope to build something that lasts.