Senior Data Engineer, Python

About This Role

This Senior Data Engineer will optimize production forecasting and operations scheduling in the energy sector by designing and building scalable data pipelines, developing robust data models, and implementing numerical routines for complex mathematical models.

Responsibilities

• Design, build, and maintain scalable data pipelines for ingesting, transforming, and validating time-series data related to well performance, sensor readings, and operational logs.
• Develop robust, high-performance data models using PyArrow and Pandas for efficient analysis and transfer.
• Implement data quality and schema validation using Pydantic to ensure data integrity across all stages of the pipeline.
• Manage and optimize data storage and retrieval in MongoDB, and integrate with cloud-native platforms like GCP BigQuery or Snowflake where applicable.
• Build, deploy, and maintain high-performance asynchronous microservices and prototypes using FastAPI or Flask to serve complex optimization and scheduling model predictions.
• Use Postman for testing, documenting, and automating API workflows.
• Containerize and orchestrate applications using Docker and manage deployment on Google Cloud Platform (GCP).
• Collaborate with reservoir and operations teams to translate complex scheduling and logistics problems into mathematical models.
• Implement numerical routines and simulations efficiently using NumPy for use in production environments.
• Apply strong logical and analytical reasoning to debug, validate, and interpret the outputs of operational scheduling algorithms.

Requirements

• Deep Python expertise
• Mastery of modern data processing and API frameworks
• Strong foundational understanding of mathematics, reasoning, and petroleum engineering principles
• Proven ability to work with mathematical modeling, optimization, and time-series analysis (Linear and Mixed-Integer Programming, Probability and Statistics, Algorithmic Complexity and Performance Reasoning)
• Experience with scalable data pipelines, data models using PyArrow and Pandas, data quality with Pydantic
• Experience with MongoDB and cloud-native platforms like GCP BigQuery or Snowflake
• Experience building microservices with FastAPI or Flask
• Experience with Postman for API testing
• Experience with Docker and Google Cloud Platform (GCP)
• Experience implementing numerical routines and simulations using NumPy

Qualifications

• Bachelor’s or Master’s degree in Petroleum Engineering, Computer Science, Mathematics, Operations Research, or related quantitative field, or equivalent experience.

Nice to Have

• Solid understanding of well operations, drilling logistics, production data, and scheduling workflows
• Experience working with large-scale or streaming datasets
• Experience with mathematical modeling and optimization libraries (SciPy, PuLP, OR-Tools)
• Experience setting up CI/CD pipelines and container deployments on GCP

Skills

* Required skills