Senior Data Engineer
Key Skills for This Role
Full Job Posting
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
Responsibilities
- 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.
About NOV
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