Data Engineer

Fervo Energy Company • Houston, TX

5h • Hybrid

About The Position

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.

Requirements

• Bachelor’s or Master’s 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. 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 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

Nice To Haves

• 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

Responsibilities

• 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
• 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
• 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
• 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

Benefits

• medical
• dental
• vision
• life
• short-term and long-term disability
• flexible paid time off
• paid parental leave
• incentive stock options program
• bonus incentive program
• 401(k) plan with an employer match

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level