Analytics Engineer II
Overview
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.
Requirements
- Degree or Diploma: Computer Science, Engineering, Mathematics, or a related field (Essential)
- 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)
- 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)
Responsibilities
- 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
How to apply
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