Senior Data Analytics Engineer - 1 Position at M-Gas

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