Data Engineer

at  Swish Maintenance Limited

Take the next step in your IT career by joining Swish as our Data Engineer!
The Data Engineer will spearhead our ERP migration project. The ideal candidate will serve as the lead data strategist, focusing on identifying and integrating datasets essential for our ERP system. This role involves close collaboration with the ERP implementation team to reimagine data processes and ensure the delivery of actionable insights to decision-makers.
This is a full-time/permanent position that will stay on following the completion of the ERP migration project in the role of Data Engineer as part of our Data Services team.
What does the Data Engineer do?

ERP PROJECT - DUTIES & RESPONSIBILITIES:

• Data Strategy and Integration: Serve as the lead data strategist to identify and integrate datasets necessary for ERP migration.
• Collaboration: Work closely with the ERP team to redesign data processes and ensure optimal data utilization.
• Analytics Needs: Understand and maintain focus on the ERP team’s analytics needs, including critical metrics and KPIs, delivering actionable insights.
• Data Visualization: Assist in creating interactive visualizations through data interpretation and analysis, including reporting components for the ERP project.
• Stakeholder Engagement: Collaborate with stakeholders across departments to address data journey gaps and pain points related to the ERP project.
• Data Management: Oversee data management, warehousing, integration, migration, business intelligence, analytics, and delivery across ERP projects.

DATA ENGINEER POST ERP PROJECT - DUTIES & RESPONSIBILITIES:

• System Evaluation: Evaluate internal systems for efficiency, problems, and inaccuracies; develop and maintain protocols for data handling, processing, and cleaning.
• Analytical Experiments: Execute analytical experiments to solve problems in finance, sales, and distribution systems.
• Algorithm Development: Devise and utilize algorithms and models to mine data stores; perform data and error analysis to improve models.
• Machine Learning and Predictive Analytics: Share ideas regarding machine learning and advise on using predictive analytics.
• Data Modeling: Develop data models that enhance processes and add strategic value to the business.
• Process and Technology Examination: Examine data, processes, and technologies to determine the current state and critical problems of internal business functions and distribution clients.
• Research and Recommendations: Research and recommend training, equipment, and technology to improve data use.
• Training and Mentoring: Train and mentor team members in data input, use, meaning, and analysis.
• Advanced Analytics Systems: Develop, implement, and maintain advanced analytics systems.
• Automated Reporting: Create automated data reports, including documentation, toolkits, and FAQs.
• Data Management and Governance: Guide the organization in data management and governance.
• Data Strategy Communication: Communicate data strategies to business leaders.
• Trend and Opportunity Analysis: Identify trends and opportunities for growth through analysis of complex datasets.
• Organizational Method Evaluation: Evaluate organizational methods and provide source-to-target mappings and information-model specification documents.
• Proactive Data Analysis: Proactively analyze data to answer key questions for stakeholders, focusing on driving business performance and identifying areas for improvement in efficiency and productivity.

What do you need?

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field is required.
• Master’s degree in Data Science, Analytics, Computer Science, or a related field is preferred.
• Relevant certifications in data engineering, data science, or ERP systems are advantageous.
• Proven experience as a Data Engineer, with a strong track record in data management, warehousing, and integration.
• Demonstrated experience in leading ERP migration projects, including data strategy and integration.
• Extensive experience with Snowflake, Matillion, and Sigma.
• Strong understanding of analytics and data science.
• Proficiency in data mining, mathematics, and statistical analysis.
• Advanced experience in pattern recognition and predictive modeling.
• Experience with using AI to deliver leading-edge analytics.
• Ability to extract meaning from and interpret data.
• Experience in collaborating with cross-functional teams and stakeholders