Data Engineer (Machine Learning) at NOV

Job Description

• NOV ReedHycalog, a global leader in the design and manufacture of advanced tools for the energy sector, is seeking a talented and innovative Data Engineer with a focus on Machine Learning to join our high-performing team in Cape Town.

As a Data Engineer, you'll design, code, and deliver data products that drive operational efficiency and informed decision-making. Key responsibilities include:

• Technical Communication - Explain statistical models and data products to both technical and non-technical stakeholders; represent the data team across the organization.
• Product Innovation - Identify and develop new data solutions based on business needs and domain knowledge.
• Data Modelling - Build scalable, flexible data models for complex processes using standard database tools.
• Analytics Engineering - Create dashboards and visualizations using BI tools, and promote adoption through documentation and updates.
• Machine Learning Enablement - Develop ELT pipelines and collaborate with data scientists to deploy machine learning models.
• Data Architecture - Make informed decisions on data management strategies, including stream vs. batch processing and historical data handling.

What You’ll Need

Education:

• Bachelor’s in Engineering, Computer Science, Mathematics, Physics, or a related quantitative field
• Master’s degree or advanced studies preferred

Experience:

• Minimum 2 years in a quantitative/software engineering discipline
• Strong experience developing and delivering software
• Experience in the upstream oil and gas industry is a plus

Technical Skills & Competencies:

• Proficient in a general-purpose programming language (e.g., Python, Java)
• Skilled in database query languages (e.g., SQL)
• Experience with modern data platforms (e.g., Snowflake, Databricks)
• Familiarity with machine learning concepts, statistics, and algebra
• Understanding of data architecture principles and software design
• Agile development experience and the ability to work independently on projects