Data Analyst

PipeSense

Houston (TX)

On-site

USD 75,000 - 110,000

Full time

8 days ago
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Job summary

PipeSense in Houston, TX seeks a Data Analyst to convert complex pipeline data into clear, actionable insights for operations and safety.

You will analyze monitoring data, interpret signals, review alarms, and evaluate system performance, collaborating with engineering, software, and data science teams to improve workflows and support real-time decisions.

Experience with Python, MATLAB, and SQL is preferred, with strong communication and the ability to work across multiple teams.

Qualifications

  • Master’s degree or bachelor’s in a related field.
  • Strong data analysis, signal processing and modeling abilities.
  • Experience with Python, MATLAB, SQL, or similar tools.
  • Excellent communication and cross-team collaboration skills.
  • Preferred: oil and gas pipeline data or related domains.

Responsibilities

  • Analyze pipeline monitoring and field operations data.
  • Interpret signals, investigate alarms, and identify trends or anomalies.
  • Translate analytical findings into practical operational insights.
  • Support field teams with digital and analytical expertise.
  • Improve and automate data-analysis workflows.
  • Contribute to machine learning and technology-development initiatives.

Skills

Data analysis
Signal interpretation
Statistical modeling
Independent work
Cross-team collaboration
Problem solving
Communication

Education

Master’s degree in Data Science / Engineering / Physics / Mathematics / Statistics / CS / Applied Science

Tools

Python
MATLAB
SQL
Cloud platforms

Job description

PipeSense is a leader in advanced pipeline monitoring, leak detection, and real-time pig tracking. Using cutting-edge technology, we provide continuous monitoring of pipeline assets to detect and locate leaks and operational anomalies. Our goal is to maximize operational efficiency and service life while prioritizing safety and environmental protection. We are currently looking for a Data Analyst who can turn complex pipeline data into clear, actionable operational insights to join our team.

ABOUT THE ROLE

Based at our Houston, TX office, the Data Analyst will support pipeline monitoring operations through data analysis, signal interpretation, alarm review, and system performance evaluation. This role requires strong analytical ability to interpret complex field operation data, practical engineering judgment, and a self-starting work attitude.

The Pipeline Data Analyst will work closely with engineering, software, and data science teams to improve pipeline monitoring performance, automate workflows, support data-driven real-time operations, and potentially be involved in the development of new technologies.

KEY RESPONSIBILITIES

  • Analyze pipeline monitoring and field operations data
  • Interpret signals, investigate alarms, and identify trends or anomalies
  • Translate analytical findings into practical operational insights
  • Support field teams with digital and analytical expertise
  • Improve and automate data-analysis workflows
  • Contribute to machine learning and technology-development initiatives

QUALIFICATIONS

  • Master’s degree, or a bachelor’s degree in Data Science, Engineering, Physics, Mathematics, Statistics, Computer Science, Applied Science, or a related field
  • Proficiency in data analysis, signal processing, and statistical modeling
  • Experience with Python, MATLAB, SQL, or similar analytical tools
  • Strong analytical, problem-solving, and communication skills
  • Ability to work independently and collaborate across multiple teams
  • Preferred experience includes oil and gas pipelines; acoustic, vibration, or fluid-flow transient data; and cloud computing, data platforms, or dashboards.

At PipeSense, we value technical excellence, continuous innovation, straight and respectful communication, and everyone working together. If you enjoy solving complex problems and want to contribute to technology that improves pipeline safety and performance, we would like to hear from you.

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