Purpose of the Job
The Intermediate Analytics Engineer is an emerging data professional who will kick start their careers as part of the the analytics engineering team to build robust, integrated and efficient data products that delivers best-in-class use case led analytics across the organisation. The role works with a senior or lead analytics engineering to build data products and pipelines for high impact projects that delivers scale and automation and improves data availability and quality. The Intermediate Analytics Engineer has a passion for developing their skills in the art of data-, cloud- and software engineering to delivery analytics use cases to deliver business value and drive data as a competitive advantage
- Develop and maintain data pipelines using SQL and Python to produce reliable, scalable, and fit-for-purpose data products in a cloud environment
- Translate technical requirements into dependable, scalable data products or pipelines that meet the organization's needs.
- Collaborate with data scientists and analysts to understand data requirements and deliver data products for analytics use cases.
- Implement monitoring, testing, and automation procedures for data products.
- Participate in code reviews, ensuring adherence to coding standards and best practices.
- Collaborate with team members to troubleshoot data-related issues and provide solutions.
- Offer first-line support for data pipelines.
- Contribute to the creation and maintenance of documentation for datasets and analysis processes, ensuring that consistent terminology and definitions are applied to facilitate smooth collaboration within the team.
- Contribute to the development of a library of reusable software engineering artifacts aimed at expediting the creation of data products.
- Maintain technical documentation related to data products and pipelines.
- Industrialize and productionize components within a data product.
- Support DataOps initiatives within the team.
- Collaborate with the team to execute standard testing procedures and perform routine monitoring of datasets, focusing on data accuracy and quality as a significant contributor to our analytics initiatives.
- Participate in the integration and adoption of software engineering best practices within the data team, contributing to the implementation of coding standards, version control, and collaborative workflows.
Experience and Competencies
- Degree or Diploma in a Computer Science, Software Development, Engineering or a related field
- Minimum of 1 year of hands-on experience within a data team, working as a data engineer or data-focused software engineer
- Familiarity with contemporary data processing tools and technologies, contributing to the development, optimization, and productionization of data products.
- Proficiency in Python and SQL, capable of completing development tasks.
- Familiarity with fundamental concepts of utilizing Apache Spark for distributed computing, acquired through coursework or introductory projects.
- Exposure to cloud platforms such as AWS, Azure, or Google Cloud through coursework or basic projects, with a foundational understanding of cloud concepts related to compute and storage.
- Basic understanding of the concept of Infrastructure as Code and its significance in data engineering practices.
- Exposure to handling large data sets and understanding business models.
- Foundational grasp of software development best practices and version control systems (e.g., Git).
- Commitment to coding standards, encompassing code readability, effective commenting, and consistent naming conventions.
- Understanding of fundamental testing methodologies to ensure code quality.
- Openness to learning and embracing established coding patterns and best practices within the team.
- Experience collaborating within and across multifunctional teams
- Experience working in an Agile environment