Data Engineer

Job summary

Description: 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.

Benefits

Qualifications

• Master’s 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
• 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

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

Certifications

Degrees

Industry

Company size

Security clearance