Data Engineer Remote

Introduction

• A Data Engineer is responsible for designing, developing and implementing scalable and robust processes to support cloud applications and solutions. This role includes extracting, transforming, and consolidating data, developing and maintaining a data platform to support Foods Business Capabilities and thus ensure optimal cloud solutions performance and stability through support and continuous improvements

Job description

• Assist in designing and implementing scalable and robust processes for ingesting and transforming large data sets.
• Assist in the design and implementation of data pipelines from a variety of data sources and support the maintenance thereof.
• Ingest large, complex data sets that meet functional and non-functional requirements.
• Enable the business to solve the problem of working with large volumes of data in diverse formats, and in doing so, enable innovative solutions.
• Build bulk and delta data patterns for optimal extraction, transformation, and loading of data.
• Supports the organisations cloud strategy and alignment to data architecture and data governance.
• Engineer data in the appropriate formats for downstream consumption for analytics or Enterprise applications.
• Assist in the development of APIs to expose the data to Enterprise Applications and 3rd party vendors.
• Assist in identifying, designing and implementing robust process improvement activities to drive efficiency and automation for greater scalability. This includes looking at new solutions and new ways of working and being on the forefront of emerging technologies.
• Work with various stakeholders across the organisation to understand data requirements and apply technical knowledge of data management to solve key business problems.
• Provide support in the operational environment with all relevant support teams for data services.
• Create and maintain functional requirements and system specifications in support of data architecture and detailed design specifications for current and future designs.
• Support test and deployment of new services and features.

Minimum requirements

• Bachelor’s degree in computer science, Business Informatics, Mathematics, Statistics, Engineering, or equivalent. 
• 4-5 years of relevant data engineering experience. 

Technical Skills:

• Strong understanding of data structures, algorithms, and software design.
• Experience with structured and unstructured data, different data stores, and traditional RDBMS and data warehouses.
• Proficiency in cloud tools such as Redshift, Athena, Glue and Lambda.
• Good programming, performance tuning and troubleshooting skills using programming languages such as python, scala, java and C
• Practical experience with Apache Spark and cloud services (e.g., AWS, Azure, GCP).
• Experience with version control systems like Git and SVN.
• Experience with DevOps practices, including CI/CD and Infrastructure as Code.
• Exposure to AWS Platform environment such as CodePipeline, CodeBuild, CodeDeploy and Management Console

Specialised Skills:

• Data Engineering: Experience with big data, ETL, and data management processes.
• Platform Engineering: Experience with cloud platform development and maintenance.
• Cloud Engineering: Experience with cloud architecture and API development.
• DevOps: Experience with DevOps practices, architecture, and operation.
• ML Engineering: Experience with machine learning frameworks and model integration.
• Data Visualisation: Experience with data visualisation tools and techniques.

Soft Skills:

• Excellent verbal and written communication skills.
• Strong analytical and problem-solving abilities.
• Ability to work well in an agile, collaborative team environment.
• Continuous learning mindset and commitment to professional development.
• Innovative thinking and adaptability to changing business needs.

Additional Criteria

• Analytical Mindset: Demonstrates a strong analytical and problem-solving ability, capable of breaking down complex data issues and devising effective solutions.
• Collaboration and Communication: Exhibits excellent interpersonal and communication skills, with the ability to articulate complex data concepts to non-technical stakeholders. Must foster a collaborative team environment and efficiently work across different departments.
• Continuous Learning: Has a strong commitment to continuous professional development, staying ahead of the latest trends and technologies in data engineering and analytics. Willingness to pursue relevant certifications and training.
• Innovative Thinking: Displays innovative thinking and a proactive approach to identifying and pursuing opportunities to improve data processes and solutions. Comfortable proposing and experimenting with new technologies or methodologies to enhance data capabilities.
• Adaptability: Demonstrates flexibility in adapting to changing business needs and technology landscapes. Can efficiently manage multiple priorities and adapt strategies in a fast-paced environment.
• Cultural Fit: Aligns with the organisation’s culture and values, contributing positively to team dynamics and company morale. Demonstrates integrity, accountability, and a strong work ethic.