Analytics Engineer II

Purpose of the Job

Role Purpose The purpose of the Senior Analytics Engineer role is to build and model data into robust, integrated and efficient data products to support and deliver best-in-class use case led analytics across the organisation. The role works collaboratively within and across a multi-functional agile team, building data products and pipelines for high impact projects that delivers scale and automation and improves data availability and quality. The Senior Analytics Engineer is a highly analytical and conceptual individual capable of engaging in the granular detail as well as the nuances of various systems to ensure data models are fully understood and built to deliver business value and drive data as a competitive advantage.

Job Objectives

• Build data and feature marts for consumption by the data analyst, scientist, and machine learning communities.
• Set up monitoring, testing and automation procedures of data artifacts.
• Work with business stakeholders to understand business requirements and analyse and translate these into fit-for-purpose, robust and scalable data solutions.
• Organise and transform data in a meaningful way and provide additional context as necessary so that it is ready for analysis.
• Execute complex testing procedures and monitoring of datasets.
• Maintain documentation related to datasets and analysis, ensuring that everyone on the data team uses the same language and definitions.
• Productionisation of data products.
• Provide firstline data pipeline support, and contribute to overseeing data integrity and quality in pipeline.
• Consult with data analysts and data scientists to refine scripts and procedures to improve and maintain standard guidelines of the data pipeline process.
• Explore and discover opportunities to improve systems, enterprises, and processes.
• Collaborate across teams to address and improve data quality at the source.
• Foster continuous improvement by coaching adoption of software engineering best practices.
• Provide data modelling support to various teams through code reviews and training.
• Understand and apply best practises for own work while supporting the team to improve delivery standards and adopt best practices.

Qualifications

• Degree or Diploma: Computer Science, Engineering, Mathematics, or a related field (Essential).

Experience

• Data Engineering Experience: 3+ years working in a data team, with a proven track record in building and optimizing end-to-end data pipelines and architectures (Essential).
• DataOps & CI/CD: Proven experience working with Git for version control, conducting thorough code reviews, and managing deployment pipelines to promote code through environments (Dev/Staging/Prod) (Essential).
• AI-Powered Development: Experience using AI-assisted coding tools (e.g., GitHub Copilot) to accelerate model compilation, refactoring, and documentation (Preferred).

Knowledge and Skills

• dbt & Snowflake Expertise: Proficiency in developing and maintaining complex dbt projects, managing Snowflake environments, and utilizing dbt's orchestration and modeling capabilities (Essential).
• Advanced SQL: Expert-level SQL skills; ability to write clean, modular, and performant code that is easily maintainable (Essential).
• Data Modeling: Proficiency in dimensional modeling (Facts and Dims) and the ability to bridge the gap between raw data and usable business data products (Essential).
• Testing & Quality: Proven ability to implement automated testing, data contract enforcement, and gap analysis to ensure high data integrity (Essential).
• Industry Context: Retail or eCommerce industry experience (Preferred).