NOV
NOV
NOV kuria energetikos pramonei skirtą įrangą, technologijas ir skaitmenines paslaugas, o jos inovacijų istorija siekia 1862 metus. Sprendimai padeda vykdyti naftos telkinių operacijas, nes leidžia klientams saugiai ir efektyviai gaminti energiją, taip pat prisideda prie sektoriaus perėjimo prie tvaresnės veiklos. NOV pasiūloje yra MYNOV – vieno prisijungimo platforma, suteikianti naudotojams prieigą prie įvairių NOV programų ir paslaugų. Įmonė telkia gamybos, konsultavimo ir energetikos srities kompetencijas, kad padėtų klientams visame pasaulio energetikos sektoriuje.

Senior Data Engineer at NOV (Hybrid)

Build scalable data pipelines and analytics platforms for NOV’s drilling and energy operations. Support condition-based maintenance, drilling optimization, and machine learning initiatives.

Aprašymas

  • Create and optimize high-performance data pipelines for analytics, condition-based maintenance, and drilling optimization metrics.
  • Build, deploy, and support dependable batch and streaming ETL/ELT pipelines across structured, unstructured, time-series, and high-frequency sensor data.
  • Design data models, features, and architectures for reporting, operational analytics, data science, machine learning, and AI use cases.
  • Establish data quality controls, monitoring, testing, observability, and CI/CD practices for dependable production systems.
  • Partner with engineering, data science, and operations teams to prepare data, engineer features, develop analytics, and deploy predictive and machine learning models.
  • Provide hands-on technical leadership, promote sound engineering practices, and mentor colleagues.
  • Convert operational needs into scalable data and analytics solutions while managing performance, quality, and reliability risks.

Reikalavimai

  • Bachelor’s or master’s degree in computer science, computer engineering, data engineering, or a related field.
  • At least five years of professional experience in data engineering, software engineering, or a comparable data-focused role.
  • Advanced practical experience with Python, PySpark, SQL, and modern data engineering tools.
  • Track record of designing and operating scalable batch and streaming data pipelines.
  • Solid understanding of data modeling, ETL/ELT, distributed processing, and cloud or hybrid data architectures.
  • Experience with Databricks, Spark, SQL Server, data lakes, and SQL or NoSQL databases.
  • Familiarity with Git, automated testing, CI/CD, and deployment automation.
  • Technical leadership skills and the ability to collaborate across engineering, data science, and operations.
  • Preferred: Background in data science, machine learning, or AI, including deploying analytical or predictive models.
  • Preferred: Experience with predictive maintenance, anomaly detection, equipment monitoring, or operational optimization.
  • Preferred: Experience working with industrial IoT, telemetry, SCADA, WITSML, or related operational technology data.
  • Preferred: Knowledge of drilling operations, drilling optimization metrics, condition-based maintenance, and equipment reliability.
  • Preferred: Databricks or cloud data engineering certification.
  • Preferred: Experience using AWS or another major cloud platform.
  • Preferred: Familiarity with oil and gas operations and technology.

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