Data Engineer

Company Description

At the Syngenta Group, our 56,000 people across more than 90 countries strive every day to transform agriculture through tailor-made solutions for the benefit of farmers, society and our planet – making us the world's most local agricultural technology and innovation partner.

Purpose

• Develop and maintain robust data pipelines and infrastructure to support the creation of a supply chain digital twin.
• Enable accurate, real-time, and predictive insights by integrating and transforming data from diverse sources.
  

Accountabilities

• Design and implement data pipelines to integrate real-time and historical data from multiple sources, including IoT devices, ERP systems, and external data feeds, into the digital twin environment.
• Assist with data extraction, transformation, and loading (ETL/ELT) processes using modern tools and frameworks.
• Ensure the accuracy, completeness, and timeliness of data feeding into the digital twin by implementing robust data validation, monitoring, and quality assurance processes.
• Collaborate with supply chain analysts and simulation experts to model and optimise the digital twin, enabling predictive insights and decision-making for supply chain performance improvements.
• Collaborate with data analysts, data scientists, and stakeholders to understand data requirements and provide technical support.
• Develop and maintain technical documentation for data processes and infrastructure.
• Assist in implementing and maintaining data governance and security best practices.
• Contribute to the optimisation of databases and query performance.
• Support the integration of third-party data sources and APIs.
• Participate in team code reviews, ensuring quality and adherence to standards.
• Stay up to date with emerging technologies, tools, and best practices in data engineering.
  

Qualifications

Required Knowledge & Technical Skills

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related discipline (or equivalent practical experience).
• Proficient in using SQL for designing, developing, and optimising queries to manage and manipulate data effectively
• Skilled in Python programming for data analysis, automation, and developing efficient solutions, with experience in libraries such as Pandas, NumPy, and SQLAlchemy to support data-driven projects.
• Understanding of ETL processes and data pipeline design.
• Basic knowledge of data modelling, warehousing, and big data technologies.
• Strong problem-solving and analytical skills.
• Proficiency in Microsoft Office: Word, Excel, PowerPoint, SharePoint, Teams.
• Experience using virtual meeting & facilitation tools, such as Zoom, Mural/LucidChart/Miro, Menti, is advantageous.
  

Required Experience

• Previous internship, placement, or project experience in data engineering, data science, software development, or a related field (desirable).
• Familiarity with database design, management and querying (SQL, NoSQL & Cypher).
• Exposure to cloud platforms such as AWS, Azure, Google Cloud, or Data Bricks (preferred but not essential).
  

Critical Success Factors

• Excellent communication and collaboration abilities to work within a team environment.
• Eagerness to learn and adapt to new tools and technologies in a fast-paced environment.
• Ability to manage a busy workload and multiple tasks, balancing parrallel projects through effective organisation and time management skills to ensure desired outcomes are fully achieved on time.
• Complete activities to a high standard, demonstrating a consistently high level of attention to detail.
  

Additional Information