IC6 - Sr Staff Data & AI Architect

spin; Ciudad de México; Vor Ort; MXN 1.800.000 - 3.000.000; Vollzeit

Zusammenfassung

Spin is seeking a Senior Staff Data & AI Architect to spearhead the evolution of its data and AI ecosystem. You will architect modular, interoperable systems bridging engineering, data modeling, and ML across business domains, guiding infrastructure and governance to enable enterprise-scale AI.

You will lead platform Architecture for Databricks on AWS, govern standards, and mentor global teams to deliver high-impact, scalable solutions in an AI-first organization.

Qualifikationen

• Bachelor's or Master's degree required in a technical field such as Computer Science, Software or Systems Engineering, AI, Data Science, or related.

Aufgaben

• Define, maintain, and communicate the target-state data and AI architecture and multi-year technology roadmap.
• Chair design authority forums and architecture review processes; document decisions with ADRs and cost/risk trade-offs.
• Assess the existing platform landscape to identify debt, redundancy, and gaps; sponsor optimization initiatives.
• Develop and govern reference architectures, design patterns, and engineering guidelines (medallion, data contracts, data products, event-driven design).
• Design composable architectures with defined interfaces (APIs, event streams) enabling integration with platforms and partners.
• Own the architecture of the Databricks Data Intelligence Platform (Unity Catalog, Delta Lake, Workflows) and its AWS integration.
• Lead enterprise AI architecture including model lifecycle, feature engineering, model registry, serving, and safety controls.
• Provide architectural direction for Retail and Fintech use cases with high-volume, low-latency workloads.
• Incorporate data privacy, security, lineage, auditability, and Responsible AI principles into designs.
• Mentor Architects, Data Engineers, Data Modelers, DataOps, and ML Engineers; drive communities of practice.

Ausbildung

Bachelor's or Master's degree in Computer Science, Software or Systems Engineering, Artificial Intelligence, Data Science, or a related field.

Jobbeschreibung

Objective of the Role

Spearhead the next evolution of Spin's data and AI ecosystem as our Sr. Staff Data & AI Architect . In this top-tier technical role, you'll serve as the master strategist driving our AI-First vision from concept to high-scale reality. You will architect the modular, interoperable systems that seamlessly connect engineering, data modeling, and machine learning across every business domain. If you thrive on solving complex technical challenges, setting industry-leading standards, and mentoring world-class teams to ship real impact, this is your stage.

Main Responsibilities

• Enterprise Architecture & Roadmap: Define, maintain, and communicate the target-state data and AI architecture and multi-year technology roadmap, covering lakehouse, streaming, MLOps/LLMOps, and governance capabilities in alignment with corporate strategy.
• Architecture Governance: Chair design authority forums and architecture review processes; document decisions through Architecture Decision Records (ADRs) and ensure solutions comply with approved standards, with documented trade-off, cost, and risk analysis.
• Continuous Improvement & Rationalization: Assess the existing platform landscape and in-flight initiatives to detect technical debt, redundancy, and gaps; recommend and sponsor optimization initiatives with quantifiable benefits in performance, reliability, cost, and delivery speed.
• Reference Architectures & Standards: Develop and govern reference architectures, design patterns, and engineering guidelines (medallion architecture, data contracts, data products, event-driven design, domain-oriented models), promoting adoption across delivery teams through enablement.
• Interoperability & Composability: Design composable architectures with well-defined interfaces (APIs, event streams, open table formats, semantic layers) that reduce dependencies between components, simplify evolution and replacement of capabilities, and enable integration with Technology platforms and external partners.
• Platform Architecture (Databricks & AWS): Own the architecture and operating model of the Databricks Data Intelligence Platform (Unity Catalog, Delta Lake, Lakeflow, Workflows, Mosaic AI, Model Serving) and its integration with AWS services, including workspace strategy, networking, security, environment management, and cost governance.
• AI/ML & GenAI Architecture: Lead the architecture of enterprise AI solutions, including model lifecycle management, feature engineering, model registry and serving, observability and drift detection, retrieval-augmented generation, agentic workflows, vector search, and AI evaluation and safety controls.
• Industry Solution Architecture: Provide architectural direction for strategic Retail use cases (customer 360, personalization, forecasting, pricing, loyalty) and Fintech use cases (credit risk, fraud detection, payments, real-time decisioning), handling high-volume, low-latency workloads.
• Security, Compliance & Responsible AI: Incorporate data privacy, security, lineage, auditability, and responsible AI principles into every design, ensuring alignment with applicable regulation and standards (e.g., CNBV, CONDUSEF, LFPDPPP, PCI-DSS) and model risk management policies.
• Technical Leadership & Mentorship: Act as the technical lead for Architects, Data Engineers, Data Modelers, DataOps, and ML Engineers through coaching, design reviews, and communities of practice; contribute to talent assessment, hiring, and capability development.
• Stakeholder Engagement: Collaborate with Product, Credit, Risk, Technology, Security, and Business leadership to translate business objectives into technical strategy and ensure alignment with enterprise architecture.
• Reliability, Scalability & Cost Optimization: Define non-functional requirements and service level objectives (SLOs), resilience and disaster recovery approaches, and continuity practices, observability, and FinOps controls for batch and real-time workloads.
• Emerging Technology Evaluation: Evaluate emerging technologies and approaches (generative AI, AI agents, feature stores, data mesh, open lakehouse formats) through proofs of concept and benchmarking, and issue adoption recommendations.
• Documentation & Knowledge Management: Maintain the architecture repository, blueprints, and technical documentation as the authoritative source of truth.
• Engineering Culture: Foster ownership, operational accountability, and a DevOps/DataOps culture within engineering teams.
• AI-First Mindset & Enablement: Champion Spin's AI-First pillar by embedding AI as a default consideration in architecture and design decisions, encouraging the use of AI-assisted tools across the engineering lifecycle, and fostering AI literacy and an experimentation-driven mindset among teams and stakeholders.

Required Knowledge and Experience

Education: Bachelor's or Master's degree in Computer Science, Software or Systems Engineering, Artificial Intelligence, Data Science, or a related field.

Experience