Data Engineer (4 Months)

Job Description

• Process Identification & Automation: Quickly identify repetitive, manual tasks and processes for short-term automation, primarily using tools like Robotic Process Automation (RPA) or scripting.
  
  
• Solution Development & Deployment: Design, develop, and implement streamlined, high-impact automation solutions to deliver immediate efficiency gains
• Stakeholder Alignment: Collaborate closely with business users to gather requirements and ensure automated solutions address immediate operational pain points.
  
  
• Data Engineering & Transformation: Develop and implement techniques and analytics applications to transform raw business data into meaningful, structured information using data-oriented programming languages (e.g., Python, R).
  
  
• Visualization & Reporting: Utilize visualization software (e.g., Tableau, Power BI) to visualize, interpret, and report key data findings.
  
  
• Dynamic Dashboard Creation: Create dynamic and interactive data reports and dashboards for stakeholders, enabling them to monitor operational performance and the immediate impact of implemented automation.
  
  

Requirements

• Academic Qualification: Master's degree in a relevant field (e.g., Data Science, Computer Science, Engineering, Technology) from an established university.
  
  
• Automation Proficiency: Hands-on experience with Robotic Process Automation (RPA) tools (e.g., UiPath, Power Automate) or other workflow automation platforms.
  
  
• Programming Skills: Expertise in data-oriented programming languages, specifically Python (for scripting and data manipulation) and SQL (for data extraction and querying).
  
  
• Data Visualization: Demonstrated ability to design and build dynamic dashboards and reports using tools like Tableau or Power BI.
  
  
• Process Analysis: Proven ability to quickly analyze business processes, identify automation opportunities, and gather precise user requirements.
  
  
• Data Transformation: Experience with data cleaning, transformation, and structuring techniques to convert raw data into a format suitable for reporting and analysis.
  
  
• Communication: Strong skills in clearly translating technical requirements, data findings, and automation impacts to non-technical business stakeholders.