Regularly design, develop, and maintain data pipelines and ML workflows to support operational and analytical needs
Write clean, efficient, and well-documented Python code for data processing, model development, and automation tasks
Analyze and interpret complex datasets, including time-series and machine health data to surface meaningful insights and support decision-making
Build, validate, and monitor AI/ML models in production, proactively identifying and resolving performance issues
Communicate technical findings and recommendations clearly to both technical and non-technical stakeholders across teams
Participate in cross-functional meetings and planning sessions to align data engineering efforts with broader business goals
Stay current with advancements in AI/ML techniques, tools, and industry-specific applications, particularly within power, oil, and gas environments
Document processes, models, and systems in a way that supports knowledge sharing and team continuity

5–7 years of experience in data engineering, data science, or a related technical role
3+ years of hands-on experience with AI/ML modeling, including building and deploying models in production environments
Strong proficiency in Python for data engineering and machine learning workflows
Experience designing and managing end-to-end ML pipelines (data ingestion → feature engineering → model training → deployment)
Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field

Data Engineer

Key skills