Current Opportunities at M-Gas

About the Job

• Engage in the end-to-end geospatial analytics process, from data collection and manipulation to analysis and modeling. Collaborate closely with the Technology teams to ensure seamless integration and alignment of geospatial analytics solutions with the company's data infrastructure.

Duties & Responsibilities

• Spatial Data Processing: Develop algorithms and scripts, utilizing SQL and Python, for processing and analyzing geospatial data. Implement spatial analytics techniques to derive meaningful insights. 
• Data Acquisition and Integration: Identify and acquire relevant geospatial datasets, developing efficient processes for integration into our systems. 
• Data Quality Management: Take charge of cleaning, transforming, and modeling geospatial data to enhance its accuracy, quality, and utility for business operations. 
• Data Visualization: Collaborate to create visually compelling maps and geospatial visualizations, developing tools for interactive data exploration. 
• Spatial Data Modelling: Fluent in tools for manipulating and visualizing both vector and raster data, with good awareness of other spatial analysis and modeling tools. 
• Cross-functional Engagement: Collaborate with technical teams, including Data Analytics, and business stakeholders to ensure geospatial analytics solutions meet business needs and drive value. 
• Data Governance: Ensure all geospatial analytics solutions adhere to data security, privacy standards, and compliance requirements, safeguarding company geospatial data. 
• Innovative Geospatial Solutions Development: Innovate and implement new methodologies in geospatial data analytics, keeping Circle Gas at the forefront of industry trends.
• Geospatial Data Analytics Expertise: Good content knowledge of geospatial data analysis. Examples (not exhaustive!) could be remote sensing, routing/spatial network analysis, OpenStreetMap, Census/demographic data, parcel/assessor/address data etc.  
• Spatial Tools Mastery: Expertise in utilizing spatial tools such as ArcGIS, QGIS, and other relevant geospatial software to manipulate and analyse geospatial data effectively. 
• Technical and Analytical Mastery: Expertise working in SQL databases, Python environments and geospatial tools. Apply statistical analysis and machine learning for insightful geospatial interpretation. 
• Cross-Domain Communication: Exceptional at articulating complex geospatial insights to stakeholders, facilitating clear understanding across geospatial engineering, analysis, and technology fields. 
• Innovative Problem-Solving: Translate complex geospatial challenges into practical solutions, leveraging geospatial engineering techniques. 
• Interdisciplinary Collaboration: Proven ability to work seamlessly with Data Analytics teams, GIS analysts, data scientists, and data engineers, fostering a collaborative environment. 
• Database and Infrastructure Knowledge: Understanding of database technologies (e.g., Amazon Aurora, MongoDB, DynamoDB, SQL Server, MySQL, PostgreSQL) and engineering practices to manage and manipulate data effectively. 

Personal Attributes

• Geospatial Data Analytics Expertise: Demonstrate a minimum of 5 years of experience across geospatial analytics, engineering, and science. Proven ability to lead projects leveraging geospatial data for significant business impacts. 
• Cartography Proficiency: Extensive experience in cartography, showcasing the ability to create visually compelling and informative maps. Familiarity with cartographic design principles to effectively communicate spatial insights. 
• Spatial Tools Mastery: Expertise in utilizing spatial tools such as ARCGIS, QGIS, and other relevant geospatial software to manipulate and analyse geospatial data effectively. 
• SQL and Python Mastery: Demonstrated proficiency in SQL and Python, utilizing these languages to manipulate and analyse geospatial data, and automate analytical processes.

Academic Qualifications
Qualification Name    Level

• Master's degree in Geospatial Engineering, GIS, or a related field    Masters

Skill Qualifications
Skill    Level

• Certification in Data Analytics or Data Engineering    Expert
• Certification in Geospatial Technology    Expert

go to method of application »

About the Job

• Strengthen Circle Gas's analytical prowess by developing advanced analytics solutions. This role is crucial in dissecting vast datasets to derive insights that inform strategic decisions, leveraging a blend of statistical analysis, machine learning, and big data technologies.  
• The Data Analytics Engineer is pivotal in bridging the gap between technical data analysis and strategic business intelligence, enhancing the company's data-driven decision-making processes.

Duties & Responsibilities

• Advanced Analytics and SQL Collaboration: Work closely with the Data Engineering team to design craft, review and execute sophisticated SQL functions to efficiently extract, combine, and analyze multiple datasets, driving insights that are crucial for informed decision-making and strategic business planning. 
• Strategic Data Analysis: Employ statistical analysis and machine learning techniques to analyze large datasets, identifying trends and patterns that support business strategies. 
• Cross-functional Engagement: Engage with both technical teams and business stakeholders to ensure analytics solutions meet business needs and drive value. 
• Business Acumen: Combine technical skills with a deep understanding of the business context, ensuring that data analysis delivers relevant and actionable insights. 
• Innovative Solutions Development: Innovate and implement new methodologies in data analysis and BI to keep Circle Gas at the forefront of industry trends. 
• Data Quality Management: Take charge of cleaning, transforming, and modeling data to enhance its accuracy, quality, and utility for business operations. 
• Data Governance: Ensure that all data analytics and BI solutions adhere to data security, privacy standards, and compliance requirements, safeguarding company data. 
• Integrated Data Expertise: Demonstrates deep proficiency in data science methodologies, data analysis techniques, and data engineering principles, bridging gaps between these domains. 
• Technical and Analytical Mastery: Expertise in SQL, Python, and BI tools, paired with the ability to apply statistical analysis and machine learning for insightful data interpretation. 
• Database and Infrastructure Knowledge: Robust understanding of database technologies (e.g., Amazon Aurora, MongoDB, DynamoDB, SQL Server, MySQL, PostgreSQL) and data engineering practices to manage and manipulate data effectively. 
• Cross-Domain Communication: Exceptional at articulating complex data-driven insights to stakeholders, facilitating clear understanding across data science, analysis, and engineering fields. 
• Innovative Problem-Solving: Skilled in translating complex data challenges into practical solutions, leveraging data science and engineering techniques. 
• Interdisciplinary Collaboration: Proven ability to work seamlessly with data scientists, data analysts, and data engineers, fostering a collaborative environment. 
• Adaptive Learning: Committed to continuous professional development in data science, data analysis, and data engineering technologies and trends. 

Personal Attributes

• Demonstrated leadership in managing large-scale database systems and complex data integration projects. 
• Proven mentorship of junior data analysts and analytics engineers, enhancing team capabilities and fostering a data-driven culture. 

Academic Qualifications
Qualification Name    Level

• Master's degree in Computer Science, Data Science, Statistics, or related field    Masters

Skill Qualifications
Skill    Level

• Certification in Machine Learning or Data Engineering    Proficient
• Data analytics    Expert