The posting
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, observability practices, 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: 10+ years of progressive experience in data engineering, data architecture, or software architecture, including 5+ years in an enterprise or solution architect role and 3+ years designing AI/ML or large-scale data platforms in production. Demonstrated record of delivering architectures adopted across multiple teams or business units.
- Industry Experience: Proven experience in Retail and Fintech, or comparable high-volume regulated environments.
- Technical Skills
- Databricks (Mandatory): Expert proficiency in the Databricks platform for data and AI workloads: Delta Lake, Unity Catalog, Lakeflow/Delta Live Tables, Structured Streaming, Photon, MLflow, Feature Store, Mosaic AI, and Model Serving; platform administration, performance tuning, and cost optimization.
- Cloud Platforms (Mandatory: AWS): Expert, hands-on proficiency in AWS (S3, Kinesis, MSK, Glue, IAM, VPC, Lambda, EKS) and its integration with Databricks; working knowledge of GCP (BigQuery, Pub/Sub, Vertex AI) is desirable.
- Architecture Frameworks & Methods: Working command of TOGAF, cloud Well-Architected Frameworks, DAMA-DMBOK, C4 modeling, and ADR practices; Domain-Driven Design, data mesh, and data product concepts; Lambda/Kappa patterns; event-driven and microservices integration; API and data contract design.
- Data Modeling & Storage: Expert in dimensional, relational, NoSQL, and lakehouse data modeling; open table formats (Delta Lake, Apache Iceberg, Apache Hudi); semantic layers and Modern Data Stack tooling.
- Streaming & Integration: Advanced knowledge of streaming and event-driven platforms (Kafka, Kinesis, Pub/Sub), change data capture (CDC), and enterprise integration patterns.
- AI/ML & GenAI: Architecture of MLOps/LLMOps pipelines, automated retraining, model monitoring, RAG, vector databases, LLM orchestration, and AI governance.
- Governance, Security & Delivery: Data governance, cataloging, lineage, privacy by design, access control and encryption models, Infrastructure as Code (Terraform), CI/CD for data and AI, and FinOps practices.
- Leadership & Soft Skills: Strategic thinking and strong abstraction skills; ability to articulate and substantiate architectural decisions to technical and executive audiences; stakeholder management and influence; coaching and mentoring; analytical rigor and sound judgment; strong AI-First mindset, with demonstrated adoption of AI tools and a drive to promote AI-enabled ways of working.
- Languages: Intermediate English proficiency.
En Spin estamos comprometidos con construir un lugar de trabajo diverso e inclusivo.
Creemos en la igualdad de oportunidades y promovemos un entorno libre de discriminación por motivos de raza, origen nacional, género, identidad de género, orientación sexual, discapacidad, edad o cualquier otra condición legalmente protegida.
