Senior Data Engineer

In this role, you will lead the development of a robust data ecosystem that transforms drilling, equipment, and operational data into actionable insights. You will work closely with engineers, data scientists, and operational experts to deliver reliable data solutions and enable analytics and ML/AI applications across NOV's operations.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Data Engineering, or related field.

• Minimum 5+ years of professional experience in data engineering, software engineering, or related data-focused roles.

• Strong hands-on experience with Python, PySpark, SQL and modern data engineering technologies.

• Proven experience designing and operating scalable batch and streaming data pipelines.

• Strong knowledge of data modeling, ETL/ELT, distributed processing, and cloud/hybrid data architectures.

• Experience with platforms and technologies such as Databricks, Spark, SQL Server, Data Lakes, and SQL/NoSQL databases.

• Experience with software engineering practices including Git, automated testing, CI/CD, and deployment automation.

• Demonstrated technical leadership and ability to collaborate across engineering, data science, and operational teams.

Preferred Qualifications

• Experience with data science, machine learning, or AI, including developing or productionizing analytical and predictive models.

• Experience with predictive maintenance, anomaly detection, equipment monitoring, or operational optimization.

• Experience with industrial IoT, telemetry, SCADA, WITSML, or other operational technology data.

• Knowledge of drilling operations, drilling optimization KPIs, condition-based maintenance, and equipment reliability.

• Databricks or cloud data engineering certification.

• Experience with AWS or other major cloud platforms.

• Knowledge of oil and gas industry operations and technology

• Design, develop, and optimize scalable, high-performance data pipelines supporting analytics, condition-based maintenance, and drilling optimization KPIs.

• Build, deploy, and maintain reliable batch and streaming ETL/ELT pipelines for structured, unstructured, time-series, and high-frequency sensor data.

• Develop data models, features, and architectures that support reporting, operational analytics, data science, machine learning, and AI applications.

• Implement data quality controls, monitoring, testing, observability, and CI/CD practices to ensure reliable production solutions.

• Work directly with engineers, data scientists, and operational experts to prepare data, engineer features, develop analytical solutions, and productionize predictive and machine learning models.

• Provide technical leadership through hands-on development, establish engineering best practices, and mentor other team members.

• Translate operational requirements into scalable data and analytical solutions while proactively identifying and addressing performance, data quality, and reliability risks.

• Design, develop, and optimize scalable, high-performance data pipelines supporting analytics, condition-based maintenance, and drilling optimization KPIs.

• Build, deploy, and maintain reliable batch and streaming ETL/ELT pipelines for structured, unstructured, time-series, and high-frequency sensor data.

• Develop data models, features, and architectures that support reporting, operational analytics, data science, machine learning, and AI applications.

• Implement data quality controls, monitoring, testing, observability, and CI/CD practices to ensure reliable production solutions.

• Work directly with engineers, data scientists, and operational experts to prepare data, engineer features, develop analytical solutions, and productionize predictive and machine learning models.

• Provide technical leadership through hands-on development, establish engineering best practices, and mentor other team members.

• Translate operational requirements into scalable data and analytical solutions while proactively identifying and addressing performance, data quality, and reliability risks.