Data ArchitectureData Engineer

该职位是迪卡侬上海的数据架构/数据工程师，负责设计可扩展、弹性的数据架构（如数据湖仓、数据网格），构建端到端数据管道，并推动MLOps基础设施

Job Purpose Drive large-scale technical excellence and pragmatism by ensuring the data team's technical solutions are robust, efficient, and meet organizational needs. Foster cross-team synergy and growth by mentoring teammates, facilitating collaboration, and championing best practices across multiple data teams. Responsibilities: Architect and design complex data systems 1.Define and evolve scalable, resilient, and cost-effective data architectures (e.g., data lakehouses, data mesh domains) to support diverse business needs and growing data volumes. 2.Design end-to-end data pipelines, including ingestion, transformation, storage, and consumption layers, optimizing for performance, reliability, and data quality. 3.Select and evaluate appropriate data technologies (e.g., cloud platforms, databases, orchestration tools) that align with architectural principles and Decathlon's multi-cloud strategy. 4.Architect and enable foundational MLOps infrastructure, designing scalable systems for feature engineering, model training data pipelines, and deployment monitoring. 5.Create detailed technical specifications, data models, and documentation for new and existing data systems. 6.Conduct technical reviews and provide constructive feedback on architectural designs and implementations proposed by other teams. Drive technical strategy and roadmap 1.Translate Decathlon's business objectives and challenges into a clear, actionable technical strategy for data, identifying opportunities for innovation and competitive advantage. 2.Develop and champion the long-term technical roadmap for data platforms, tools, and practices, aligning it with overall engineering and product strategies. 3.Research and evaluate emerging data technologies, industry trends, and best practices (e.g., GenAI, real-time analytics) to assess their potential impact and applicability to Decathlon. 4.Lead technical FinOps initiatives for the data domain by establishing patterns for cost attribution, budget tracking, and optimization of cloud services. 5.Collaborate closely with product owners and business stakeholders to deeply understand their data needs, translating them into technical requirements and solutions. 6.Identify and advocate for necessary investments in infrastructure, tooling, and talent development to support strategic data initiatives. Solve cross-cutting technical challenges 1.Diagnose and resolve highly complex, ambiguous, and critical technical issues impacting multiple data systems or teams, often involving root cause analysis and preventative measures. 2.Assess and mitigate technical risks associated with data architecture, new technologies, and complex system integrations. 3.Lead incident response and post-mortem analyses for major data-related outages or performance degradations, implementing solutions to prevent recurrence. 4.Identify and address technical debt, architectural shortcomings, and scalability bottlenecks across the data landscape. 5.Navigate ambiguity by defining technical roadmaps for complex, future-facing business problems with no clear existing solution. 6.Develop reusable components, libraries, and frameworks to standardize solutions for common data challenges. Provide expert technical consultation and guidance to various data and product teams facing intricate data problems. Mentor and elevate engineering talent 1.Provide direct technical mentorship and coaching to senior and principal data engineers, fostering their growth in architecture, system design, and problem-solving. 2.Contribute to the development of career pathways and technical competency frameworks for data engineers, setting clear expectations for progression. 3.Lead by example in writing high-quality, well-tested, and maintainable data code and infrastructure as code. 4.Organize and deliver technical workshops, brown bag sessions, and knowledge-sharing initiatives within the data community. 5.Participate in the recruitment and interviewing process for data roles, assessing technical excellence and cultural fit. Champion best practices and standards 1.Define, implement, and evangelize data best practices across data quality, data governance, security, observability, and MLOps principles. 2.Establish and enforce coding standards, documentation guidelines, and operational procedures for data pipelines and infrastructure. 3.Promote a culture of data ownership, accountability, and continuous improvement within and across data domains, aligning with Decathlon's data mesh ambitions. 4.Develop and maintain shared tools, templates, and frameworks that facilitate adherence to established best practices. Conduct regular audits and reviews to ensure compliance with data governance policies and security requirements.