Data Engineer

Fervo is building the most cost-effective repeatable geothermal power plants in the world. Scaling that mission depends on a trustworthy well-governed data foundation that turns raw sensor signals drilling and completions records and power plant telemetry into reliable decision-ready information. The Data Engineer within the Data & AI team designs builds and operates the pipelines models and platforms that move data from the field to the people and systems that act on it engineers operators data scientists and the analytical and agentic applications built on top.

The Data Engineer owns data products end to end from ingestion and modeling through quality governance and serving to monitoring in production. Working across Data Science AI Engineering IT Infrastructure domain SMEs and business stakeholders this role establishes reusable patterns for real-time and batch processing IoT/historian integration data quality and entity linkage and self-service analytics on our Azure Databricks and Snowflake stack. Success requires strong hands-on engineering depth in distributed data processing sound data modeling and architecture judgment and pragmatism about what to ship versus what to defer.

Responsibilities

Data Pipeline & Platform Engineering

• Design build and operate scalable batch and real-time/streaming data pipelines on Databricks and Azure Data Factory landing data in Azure Data Lake Storage (ADLS) and Snowflake
• Implement the medallion (bronze/silver/gold) architecture using Delta Lake and Delta Live Tables with reliable incremental processing schema evolution and change data capture
• Build and tune Apache Spark jobs (PySpark/Spark SQL) for large-scale parallel data processing partitioning shuffles caching broadcast joins and cost/performance optimization
• Ingest and process high-volume IoT and historian data (sensor SCADA time-series) via streaming frameworks (Structured Streaming Event Hubs/Kafka) and micro-batch patterns

Data Modeling Quality & Governance

• Model curated analytics-ready datasets and serving layers that are well-documented performant and easy for downstream consumers to use
• Implement automated data quality frameworks validation profiling anomaly detection freshness and completeness checks with clear alerting and remediation paths
• Build entity resolution and record linkage logic to unify wells pads assets equipment and events across heterogeneous source systems
• Establish and enforce data governance using Unity Catalog access controls lineage data classification and a shared semantic/metadata layer that makes business concepts queryable and trustworthy

Reliability CI/CD & Production Operations

• Apply software engineering discipline to data: version control code review automated testing and CI/CD pipelines (Azure DevOps or GitHub Actions) for data and infrastructure
• Implement monitoring logging and observability across pipelines to support debugging SLA tracking cost monitoring and continuous improvement
• Support production incidents and platform-level issues impacting data pipelines and downstream consumers; develop runbooks and reduce toil through automation

Analytics Enablement & Collaboration

• Partner with analysts and stakeholders to deliver datasets and semantic models that power dashboards in Power BI and Spotfire
• Collaborate with Data Science and AI Engineering to provision clean governed feature-ready data for ML and agentic workflows
• Translate domain problems from drilling completions production geophysics and power plant operations into well-scoped reliable data products with clear ownership and success metrics

Required Qualifications

• Bachelors or Masters degree in Computer Science Data Engineering Software Engineering Information Systems Applied Mathematics Physics or a related technical field or equivalent practical experience demonstrated through a portfolio of shipped data systems. Masters preferred.
• 2 years of hands-on experience building and operating production data pipelines not just prototypes or notebooks
• Deep understanding of the Apache Spark framework and distributed parallel data processing partitioning shuffles joins caching and performance tuning at scale
• Strong programming skills in Python (PySpark) and SQL including writing testable maintainable production code
• Hands-on experience with Databricks including Delta Lake Delta Live Tables and Unity Catalog
• Experience with Azure Data Factory and Azure Data Lake Storage (ADLS) or equivalent cloud data services with willingness to work in our Azure-first environment
• Experience with cloud data warehousing on Snowflake (or equivalent: BigQuery Redshift Databricks SQL)
• Experience building both real-time/streaming and batch pipelines (Structured Streaming Event Hubs/Kafka or similar)
• Solid data modeling skills (dimensional medallion/lakehouse or normalized) and a track record of building well-documented consumable datasets
• Experience implementing data quality validation and observability for pipelines
• Strong Git and CI/CD experience (Azure DevOps or GitHub Actions) including version control discipline code review and automated testing
• Experience delivering data to BI/analytics tools such as Power BI and/or Spotfire

Preferred Qualifications

• Experience with data governance and cataloging Unity Catalog Microsoft Purview lineage tooling and semantic/metric layers (Snowflake Semantic Views Databricks Metric Views dbt Semantic Layer)
• Experience with entity resolution / record linkage and master data management across messy multi-source data
• Background in time-series data signal processing or industrial IoT (MQTT OPC UA SparkplugB) and historian systems (Canary Ignition)
• Experience with infrastructure-as-code (Terraform preferred) and containerization (Docker)
• Experience with orchestration tooling (Databricks Workflows Airflow ADF pipelines) and dbt for transformation
• Familiarity with data security and access control patterns Entra ID row/column-level security masking Key Vault-managed secrets
• Oil and gas or energy industry experience including familiarity with drilling completions production geophysics or industrial historian data
• Exposure to ML/AI data needs feature engineering feature stores or provisioning data for agentic/LLM applications
• Experience optimizing cloud data cost and performance at scale

Location

Fervo Energy is headquartered in Houston TX with growing offices in Golden CO Reno NV and Oakland CA and Salt Lake City UT. This position will be eligible for some hybrid work flexibility but regular in-office presence at theOakland orHoustonoffice 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 applicants education experience knowledge skills and abilities in addition to internal equity and geographic location. Expected Salary: $103000 $170000 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 applicants sex or other status protected by local state or federal law.

Fervo Energy is an Equal Opportunity Employer and does not discriminate on the basis of race color creed gender religion marital status registered domestic partner status age national origin ancestry physical or mental disability medical condition sex genetic information sexual orientation military and veteran status or any other consideration made unlawful by federal state or local laws. It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics or is associated with a person who has or is perceived as having any of those characteristics.ffice will be required.