Analytics Engineer

Why this role exists: The Analytics Engineer builds the trusted analytical foundations that enable high-quality reporting, analysis, data science, and decision support. Sitting between data engineering and analytics delivery, this role transforms, models, and structures raw or curated data into reliable, reusable assets ready for business use cases. This role focuses on creating well-governed, analytics-ready data layers that improve the speed, consistency, and trust in global insights.

Where you will make a difference: In this role you will: Analytics Data Modelling & Transformation: Design and maintain analytics-ready datasets and reusable data models. Transform raw data into structured datasets to support complex reporting, analysis, and modelling. Develop consistent business logic, metrics, and data definitions to ensure analytical uniformity.

Build reusable data layers to reduce duplicated effort across dashboards and model builds. Ensure datasets are scalable, documented, and fit for purpose within the enterprise data platform. Data Quality, Testing & Governance: Apply testing and reconciliation controls to ensure all analytical outputs are accurate.

Identify data quality root causes and work across teams to improve overall data health. Document data lineage, business rules, and transformations clearly. Enforce governance standards, ensuring assets use trusted sources and approved definitions.

Enablement & Collaboration: Support BI Developers and Analysts by creating consistent semantic layers for self-service. Improve self-service maturity by making data easier for business stakeholders to find and understand. Partner with Data Engineers to ensure source data availability aligns with analytical needs.

Collaborate with Data Scientists to prepare modelling-ready datasets and features. Translate analytical requirements from business partners into technical data models and reusable assets. Engineering Standards & Continuous Improvement: Standardize analytics engineering practices across transformation logic and release management.

Automate manual data preparation to reduce rework and increase reliability. Maintain version control and peer review practices for all analytics code and data assets. Stay current with modern tools like Snowflake and dbt to maintain high delivery standards.

Who you will be working with: Data & Analytics Hub Teams: Partnering with AI Engineers, Data Scientists, and Data Engineers. BI Developers & Data Analysts: Ensuring dashboards are built on consistent, trusted data models. Analytics Business Partners: Working to understand commercial context and requirements.

Other Technology Teams: Aligning engineering work with platform standards and governance. What success looks like: Trusted Foundations: Analytical datasets are reusable and easy for technical teams to utilise. Consistency: Reports and models across the business use uniform metrics and logic.

Efficiency: Manual data preparation is reduced, leading to faster delivery of priority initiatives. Stakeholder Confidence: Greater business trust in the accuracy and consistency of analytical outputs. How you will lead: Individual Contributor: Lives our Officeworks values and behavior's Proactively contributes to a safe working environment, escalates appropriately if there are unsafe conditions or inappropriate behavior Operates in line with applicable Officeworks company policies and Code of Conduct Demonstrates a strong sense of personal accountability and curiosity to learn and develop Qualifications and work experience: Essential: Education: Bachelors degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.

Experience: 5+ years in analytics engineering, data modelling, or data transformation roles. Technical Mastery: Strong SQL skills, including experience optimizing data for analytical use. Modelling Knowledge: Deep understanding of data quality, testing, documentation, and semantic layers.

Adaptability: Essential knowledge of modern platforms like Snowflake and GitHub, with a focus on high-quality delivery. Communication: Ability to translate complex analytical needs into fit-for-purpose technical models. Preferred: Modern Tooling: Experience with dbt, Airflow, or similar transformation and orchestration tools.

BI Tools: Familiarity with Power BI, Tableau, or Looker. Engineering Standards: Familiarity with CI/CD, version control, and automated testing frameworks. Business Context: Experience in a fast-paced retail or omnichannel environment.