Online Analytics Engineer

at  Kent

KNOWLEDGE/ QUALIFICATION/ TRAINING/ CERTIFICATION:

Knowledge/ Qualification/ Training/ Certification:

• A degree in data science, engineering, computer science, or a related field. An advanced degree (MSc or PhD) in data analytics with a focus on oil and gas could be advantageous.
• Oil and Gas Operations Knowledge; Strong understanding of upstream (exploration and production), midstream (transportation and storage), or downstream (refining and petrochemical) processes.
• Field Data Experience; Familiarity with common data sources in the oil and gas industry, such as SCADA (Supervisory Control and Data Acquisition) systems, well logs, and process control systems.
• Data Engineering and Management; Strong skills in managing large datasets, including experience with cloud platforms (AWS, Azure) and real-time data ingestion from IoT devices and sensors.
• Analytics and Machine Learning; Proficiency in developing machine learning models for predictive maintenance, production optimization, and anomaly detection using tools like Python, R, or MATLAB.
• Experience with Big Data Tools; Familiarity with tools like Apache Spark, Kafka, or Hadoop for processing large-scale datasets.

Programming and Scripting Skills:

• Strong programming skills in languages such as Python, SQL, or Java for building analytics pipelines, automation, and performing data analysis.
• Knowledge of scripting for quick data manipulation and integration (e.g., shell scripting).
• Data Visualization Tools; Proficiency with platforms like Power BI, Tableau, or Spotfire to create dashboards and reports.
• Database Management; Familiarity with SQL databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB) for data storage and retrieval.
• Real-Time Analytics Platforms; Experience with platforms like OSIsoft PI, Honeywell’s Uniformance, or other process data historians used in oil and gas.

OUR PURPOSE AND BELIEFS:

As Kent, we’re tackling the greatest challenge of our time - to bring our world the energy it needs in the most responsible way ever imagined.
It’s the energy of every member of our team driven by our beliefs that is making this happen. Whatever our skill, our language, or our culture. These beliefs define and direct us every day to be the very best - for ourselves, our clients, our key stakeholders, the communities we operate in and the planet:
We PLAY BIG
We thrive on EMOTIONAL AGILITY
We are FANATICAL ABOUT PERFORMANCE
We are built on INFINITE THINKING
Read more about the Purpose, Beliefs and Guiding Principles that drive us: https://kentplc.com/who-we-are/purpose-beliefs

SKILLS & RESPONSIBILITIES:

Data Integration and Management:

• Real-Time Data Collection; Capture and integrate data from sensors, drilling rigs, production facilities, pipelines, and other infrastructure into a centralized analytics platform.
• Data Cleaning and Processing; Ensure data quality by removing noise, filling gaps, and preparing data for analysis.

Developing and Maintaining Analytics Models:

• Predictive Maintenance Models: Create and update models to predict equipment failures or pipeline integrity issues, reducing downtime.
• Reservoir and Production Optimization: Develop models that can forecast production, optimize drilling, and manage reservoir performance by analysing well and reservoir data.
• Process Control and Optimization: Use data analytics to enhance process efficiency, reducing energy consumption and operational costs in refining, petrochemical, and upstream operations.

Monitoring and Anomaly Detection:

• Continuous Monitoring; Set up dashboards and alert systems to track real-time operations, identifying deviations from normal behavior.
• Anomaly Detection; Use machine learning and statistical models to detect irregularities in processes or equipment performance that could indicate potential risks.

Collaboration with Operations Teams:

• Work closely with engineers, operators, and field teams to understand operational challenges and provide actionable insights through analytics.
• Translate technical data findings into practical recommendations that can be implemented in field operations.

Reporting and Visualization:

• Dashboards and Reports; Build intuitive dashboards and reports using tools like Power BI, Tableau, or custom-built software to visualize data trends and provide decision support.

Performance Tracking:

• Regularly update performance indicators and provide insights on key metrics like production efficiency, equipment health, and energy usage.

Automation of Data-Driven Decision Making:

• Automate workflows that use real-time data for decision-making, like dynamic equipment setpoints or automated well control based on live analytics results.

In addition to the responsibilities listed herein, the employee may be required to perform other ad-hoc tasks as needed or directed by the supervisor or management. These tasks will be within the reasonable scope of the employee’s skills, capabilities, and role within the organization. The intent of this provision is to allow for flexibility and adaptability in meeting the dynamic needs of the organization, ensuring that operational requirements can be met efficiently. All such tasks will be assigned considering the employee’s current workload and with respect to their professional development.
In addition to the responsibilities listed herein, the employee may be required to perform other ad-hoc tasks as needed or directed by the supervisor or management. These tasks will be within the reasonable scope of the employee’s skills, capabilities, and role within the organization. The intent of this provision is to allow for flexibility and adaptability in meeting the dynamic needs of the organization, ensuring that operational requirements can be met efficiently. All such tasks will be assigned considering the employee’s current workload and with respect to their professional development.