Fervo Energy is seeking a Senior Data Architect to design, build, and scale the company’s industrial data platform. This is the first dedicated hire focused on industrial data architecture and implementation, with responsibility for integrating subsurface sensors, IoT systems, and facilities and power plant historian data into a unified, analytics-ready platform.

This role is hands-on in its early phase and is expected to build the initial implementation of the industrial data platform. The Senior Data Architect will evaluate and select platforms, define canonical data models, and personally build the initial production pipelines and datasets. Over time, this role will establish standards and grow a team of more engineers. In addition to industrial and time-series data, this role will help define and evolve Fervo’s broader data architecture, encompassing structured relational data as well as unstructured and semi-structured data (e.g., documents, logs, reports, and engineering artifacts), in close collaboration with the IT organization.

The role will report to the Manager, Data Acquisition & Advanced Analytics and work closely with Reservoir Engineering, Drilling & Completions, Power and Facility teams to ensure Fervo’s data is accurate, accessible, secure, and actionable.

Process Ownership & Execution

• Serve as the hands-on technical lead to design, build, and own the first production implementation of Fervo’s industrial data platform
• Architect the end-to-end industrial data platform, including ingestion, storage, processing, and access layers
• Design and implement ingestion pipelines from industrial systems such as SCADA, DCS, historian platforms, IoT and edge devices, and subsurface monitoring systems
• Build and maintain scalable data models and schemas to support high-frequency time-series data, asset and sensor metadata, events, states, and derived metrics
• Define, implement, and evolve canonical / common data models across industrial domains including assets, wells, facilities, equipment, sensors, and measurements
• Develop transformation logic for unit normalization and conversion, interpolation and resampling, time-based aggregations, and data quality validatio
• Optimize data storage, query performance, reliability, and cost for large-scale industrial and time-series workloads
• Evaluate, select, and evolve platforms and technologies (e.g., Snowflake or similar cloud data platforms, streaming and ingestion frameworks, time-series processing tools) to meet scalability, reliability, and engineering requirements
• Define strategies for ingesting, modeling, and governing unstructured and semi-structured data (e.g., documents, logs, engineering records) and linking them to asset and operational context
• Own and define the technical roadmap for scaling the industrial data platform as Fervo’s assets, use cases, and data volumes grow

Team & Culture

• Demonstrate strong technical ownership and accountability for the reliability, scalability, and correctness of the industrial data platform
• Partner closely with engineering, subsurface, operations, and analytics teams to ensure data products meet real-world operational and business needs
• Lead by example as a hands-on builder while evolving into a technical leadership and mentorship role as the team grows
• Mentor and onboard data and platform engineers, establishing best practices in architecture, data modeling, and code quality
• Contribute to a culture of high engineering standards, continuous improvement, and pragmatic decision-making in an early-stage, fast-moving environment

Required Qualifications

• B.S. in Computer Science, Engineering, or a related technical field (or equivalent practical experience)
• 8+ years of experience designing and building industrial, IoT, or time-series data platforms
• Hands-on experience integrating data from industrial systems including SCADA, DCS, and historian platforms
• Experience working with industrial and IoT protocols such as OPC-UA, MQTT, or vendor-specific APIs
• Demonstrated experience defining and implementing canonical / common data models for complex technical domains
• Strong experience architecting cloud-based data platforms (e.g., Snowflake, Databricks, BigQuery, Redshift, or similar)
• Deep understanding of time-series data modeling and analytics, including aggregation, interpolation, and performance optimization
• Proficiency in SQL and Python (or equivalent data engineering languages)
• Comfort operating in an early-stage or evolving environment and making foundational technical decisions with limited precedent
• Experience designing and working with structured data systems (RDBMS, dimensional models, enterprise data warehouses)
• Experience contributing to data strategies for unstructured or semi-structured data (e.g., documents, logs, object storage, metadata indexing)
• Background in energy, geothermal, oil & gas, power generation, or other industrial infrastructure domains preferred

Location

Fervo Energy is headquartered in Houston, TX, with growing offices in Oakland, CA; Golden, CO; Reno, NV; and Salt Lake City, UT. This position is eligible for hybrid work flexibility; however, regular in-person presence at the Houston, Golden, Reno or Oakland office will be required.

Compensation & Benefits

Fervo provides a comprehensive suite of benefits including medical, dental, vision, life, short-term and long-term disability, flexible paid time off, and paid parental leave. Additionally, Fervo offers an incentive stock options program, a bonus incentive program, and a 401(k) plan with an employer match.

Fervo Energy is providing the compensation range and general description of other compensation and benefits that the company in good faith believes it might pay and/or offer for this position based on the successful applicant’s education, experience, knowledge, skills, and abilities in addition to internal equity and geographic location. Expected Salary: $143,000 – $200,000 based on location and experience.

Fervo Energy reserves the right to ultimately pay more or less than the posted range and offer other compensation, depending on circumstances not related to an applicant’s sex or other status protected by local, state, or federal law.