• Definir la estrategia analítica y el modelo funcional de la solución. • Diseñar y construir el pipeline de ingesta y transformación sobre AWS. • Configurar la seguridad de la infraestructura (IAM). • Diseñar el modelo dimensional (star schema). • Actuar como autoridad técnica frente al cliente.

• Maintain a clear view of each team member's delivery commitments, growth plans, and internal contributions. • Develop and grow team members through regular coaching, clear expectations, and constructive feedback. Build a culture of trust, inclusion, and collaboration. • Monitor team health, morale, and workload balance, acting early to address engagement or performance concerns in partnership with practice leadership. • Maintain regular one-to-one connections with each team member, provide clear feedback, and help them remove blockers related to client work, pursuits, or internal initiatives. • Oversee staffing and resource alignment across teams, balancing utilization, development goals, client needs, and sustainable workloads. • Communicate practice priorities and organizational objectives clearly, ensuring teams understand how their work contributes to broader organizational goals. • Define interview standards and mentorship strategies, training others to apply them consistently and improving hiring outcomes and talent growth at scale. • Create and maintain development plans for team members, including stretch assignments, shadowing, certifications, and opportunities for external visibility. • Lead or support performance reviews, promotion recommendations, and compensation input for your teams, using consistent standards that reflect both impact and behavior. • Represent your teams in leadership forums, communicate expectations and decisions clearly back to the group, and bring forward patterns, risks, and successes that should shape practice strategy. • Lead architecture and technical strategy for large Azure programs and portfolios so solutions meet client goals for value, security, scalability, reliability, performance, and cost. • Define modernization blueprints and reference architectures across applications, platforms, and data, including system and subsystem designs, patterns, and standards that simplify delivery and operations over time. • Translate business and product strategy into cloud architecture roadmaps and execution plans that drive measurable outcomes and adapt based on production feedback and telemetry. • Guide major architecture decisions across programs, promote shared design standards and reusable models, and resolve high-impact technical trade-offs. • Work across teams to connect solutions into cohesive end-to-end architectures with clear ownership boundaries and integration contracts. • Provide architectural oversight during delivery through design reviews, forums, and governance, keeping cross-cutting concerns (security, performance, resilience, availability, compliance, observability, and cost) aligned across teams. • Promote modular design, clear boundaries, testability, CI/CD, and observability so non-functional requirements are treated as first-class and systems remain reliable and maintainable. • Use AI tools to accelerate discovery, design, code, tests, documentation, and analysis, and scale successful practices so teams adopt them consistently. • Define and own enterprise data platform strategy centered on Databricks or equivalent lakehouse architectures. • Establish reference architectures spanning ingestion, lakehouse, analytics, and AI/ML. • Set standards and best practices for: Databricks lakehouse design and workload isolation. Delta Lake optimization and data lifecycle management. Streaming and batch processing patterns. Unity Catalog–aligned governance and security models. Guide multi-workspace and multi-region Databricks deployments. • Ensure tight integration between Databricks, Azure-native services, and downstream analytics tools. • Oversee architectural alignment with complementary platforms (e.g., Snowflake, Fabric, Power BI).

• Design, develop, and maintain scalable data pipelines to support ingestion, transformation, and delivery into centralized feature stores, model-training workflows, and real-time inference services. • Build and optimize workflows for extracting, storing, and retrieving semantic representations of unstructured data to enable advanced search and retrieval patterns. • Architect and implement lightweight analytics and dashboarding solutions that deliver natural language query experience and AI-backed insights. • Define and execute processes for managing prompt engineering techniques, orchestration flows, and model fine-tuning routines to power conversational interfaces. • Oversee vector data stores and develop efficient indexing methodologies to support retrieval-augmented generation (RAG) workflows. • Partner with data stakeholders to gather requirements for language-model initiatives and translate into scalable solutions. • Create and maintain comprehensive documentation for all data processes, workflows and model deployment routines. • Should be willing to stay informed and learn emerging methodologies in data engineering, MLOps and LLM operations.

• Maintain data environments and processes in Azure and on-premises, ensuring continuous and reliable operation; • Provide advanced administration and support for Databricks (Azure), including troubleshooting and operational tuning; • Monitor and manage pipelines, jobs, integrations, and data routines with a preventive and corrective approach; • Implement fixes and improvements to existing processes, performing root-cause analysis and reducing recurrence; • Lead incident management and resolution (SLA, prioritization, communication, follow-up until stabilization); • Support BI and development teams, ensuring environments are ready for new releases and requests; • Continuously evolve and improve DataOps / CI/CD pipelines for data (governance, quality, and reliability); • Collaborate with infrastructure/architecture teams when needed (capacity, access, security, network, performance); • Ensure adherence to security, governance, and compliance best practices in the data context; • Propose and implement improvements in observability, monitoring, and alerting (metrics, logs, traceability).