Provides a sound understanding of Big Data application development concepts/principles, alongside a strong knowledge of concepts and principles in other technology areas.
Advanced Problem Solving & Data Analysis
Root Cause Analysis: Solves and works through complex problems and projects via in-depth evaluation of business processes, system processes, and industry standards; performs root cause analyses.
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Post-Implementation Review and Support: Engages in post-implementation analysis of business usage to ensure successful system design and functionality.
Quality Ownership: Directly impacts the business by ensuring the quality of work provided by self and teammates.
Technical Consulting: Consults with users, clients, and other technology groups on issues and recommends advanced programming solutions.
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Set the engineering standard — Establish DataOps practices: CI/CD for pipelines, testing frameworks, alerting, and documentation. This becomes the baseline the whole team works to.
Mentor the existing DE — Actively develop the junior Data Engineer through code reviews, pairing, and structured technical guidance.
Be dbt-aware — You won't own the dbt models (that's the Analytics Engineer), but you need to understand how your pipelines feed them, contribute to Bronze-layer dbt sources, and collaborate on data contracts between layers.
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Support and work with cross-functional and multidisciplinary teams in a dynamic environment. Leverage data visualization tools and techniques to maximize impact and value of data.
Manage external technical communication with partners and vendors. Contribute to improving data governance policies.
Keep current with technical and industry developments.
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Performance & Cost Optimisation: Monitor, troubleshoot, and continuously optimise data workflows to improve performance, resilience, and cost efficiency.
Data Storage & Management: Build, manage, and maintain GIGA’s Data Mart, data lake, and related data storage solutions to support department’s reporting, analytics, and advanced use cases.
Data Modelling & Architecture Collaboration: Develop and maintain data models that support analytics, operational reporting, and decision-making tailored for audit and investigation activities. Collaborate closely with Group IT and Group Digital teams on data architecture decisions to support current and future data requirements.
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Demonstrated technical leadership through design reviews, code reviews, mentoring, and troubleshooting guidance without formal team management responsibility.
Solves / works complex problems / projects through in-depth evaluation of business processes, system processes and industry standards; performs root cause analyses.
Engages in post implementation analysis of business usage to ensure successful system design and functionality.
Provides sound understanding of Big Data apps development concepts and principles and knowledge of concepts and principles in other technology areas.
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Downstream Consumers: You will directly enable the Analytics & BI Team (who partner with business units to define use cases and build BI apps) and the AI Engineering Team (who require highly structured, model-ready features), alongside various other core downstream applications dependent on your data pipelines.
Enterprise Data Modeling & Strategy Rollout: Own the vision, design, and continuous expansion of our enterprise Common Data Model (CDM). Tailor this model explicitly to the complex, multi-channel realities of a modern retail context (inventory, customer journeys, transactions, supply chain).
AI & GenAI for Data Engineering Innovation: Research, test, and integrate AI and Generative AI capabilities into the data engineering lifecycle. Utilize GenAI for automated pipeline generation, synthetic data creation, automated documentation, SQL/code optimization, and intelligent schema mapping to significantly shorten delivery timelines.
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Apply data governance, access control and data quality standards to keep enterprise data secure and trusted.
Partner with business and IT stakeholders to translate requirements into effective analytics solutions, improving data accessibility, self-service adoption, and overall user satisfaction.
Deliver high-quality technical work on assigned projects, share expertise, and mentor junior team members to help raise data literacy across teams.
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