• Develop and maintain data pipelines (ETL/ELT) using Databricks, Azure Data Factory and other components of the Azure ecosystem • Model and structure data in Data Lakes (ADLS), ensuring data quality, traceability and governance • Ingest and process data from multiple sources (APIs, monthly files, corporate systems) • Implement data solutions for: • PETROS Database (extraction and organization of pension data) • Structuring RAG for analysis of debt contracts • Processing quotation/price data (integration with external APIs such as CXL) • Collateral projects (judicial deposits, financial and real guarantees) • Reconcile purchases and sales with analysis of structured and unstructured data • Support architects in defining solutions and designing data architecture • Participate in gathering and detailing technical and functional requirements • Develop and publish APIs to expose data • Ensure best practices for versioning, CI/CD and DevOps • Work with relational databases (SQL Server, Oracle, among others)

• Own the technical architecture and roadmap for our most complex Analytics Engineering initiatives - including semantic layer design, source-of-truth consolidation, and the data foundation for AI and agent-based use cases • Architect Forward's semantic layer and metrics standards so key business KPIs are defined once, governed clearly, and consumed consistently across dashboards, models, AI agents, and downstream products • Lead the technical design of the AI-ready data platform - making the modeling, metadata, and governance decisions that make Snowflake Intelligence and other AI/agent capabilities trustworthy, performant, and production-ready • Drive technical excellence across our dbt project: model architecture, materialization and incremental strategies, performance tuning, macros, testing patterns, and CI/CD practices that scale as data volume and team size grow • Set and uphold a high bar for craftsmanship across the team - defining standards for SQL style, modeling patterns, documentation, and data quality, and modeling those standards in your own work • Mentor Senior and Analytics Engineers through hands-on code review, pairing, and design feedback - accelerating their growth into stronger technical contributors • Partner with the Manager of Analytics Engineering on technical strategy, hiring, and roadmap planning - acting as a deputy for technical decisions and unblocking the team on the hardest problems • Lead deep technical partnerships with Data Science, Data Engineering, and Core Technology - owning schema migrations, feature deployments, and streaming pipeline contributions where Analytics Engineering is on the critical path • Evaluate and operationalize high-value third-party data sources and emerging tooling (e.g., Snowflake Cortex, semantic layer frameworks, observability tools) and make recommendations that elevate the platform • Champion data governance and quality at the platform level - including dbt tests, lineage, cataloging, observability, and compliance with security and regulatory standards - so both stakeholders and AI systems can trust the numbers

• Own the technical architecture and roadmap for our most complex Analytics Engineering initiatives - including semantic layer design, source-of-truth consolidation, and the data foundation for AI and agent-based use cases • Architect Forward's semantic layer and metrics standards so key business KPIs are defined once, governed clearly, and consumed consistently across dashboards, models, AI agents, and downstream products • Lead the technical design of the AI-ready data platform - making the modeling, metadata, and governance decisions that make Snowflake Intelligence and other AI/agent capabilities trustworthy, performant, and production-ready • Drive technical excellence across our dbt project: model architecture, materialization and incremental strategies, performance tuning, macros, testing patterns, and CI/CD practices that scale as data volume and team size grow • Set and uphold a high bar for craftsmanship across the team - defining standards for SQL style, modeling patterns, documentation, and data quality, and modeling those standards in your own work • Mentor Senior and Analytics Engineers through hands-on code review, pairing, and design feedback - accelerating their growth into stronger technical contributors • Partner with the Manager of Analytics Engineering on technical strategy, hiring, and roadmap planning - acting as a deputy for technical decisions and unblocking the team on the hardest problems • Lead deep technical partnerships with Data Science, Data Engineering, and Core Technology - owning schema migrations, feature deployments, and streaming pipeline contributions where Analytics Engineering is on the critical path • Evaluate and operationalize high-value third-party data sources and emerging tooling (e.g., Snowflake Cortex, semantic layer frameworks, observability tools) and make recommendations that elevate the platform • Champion data governance and quality at the platform level - including dbt tests, lineage, cataloging, observability, and compliance with security and regulatory standards - so both stakeholders and AI systems can trust the numbers

• Convey the strategic advantages and technical application of Snowflake's Analytics capabilities, address client inquiries, and conduct follow-up engagements to facilitate sales progression. • Articulate Snowflake's Analytics functionalities and provide a comparative analysis with competitor offerings for prospective clients. • Ascertain client-defined success metrics for the adoption and utilization of Snowflake's Analytics features. • Recognize, address, and reconcile discrepancies between Snowflake's methodologies and client-specific requisites concerning Analytics features. • Provide comprehensive resolutions to client objections and concerns. • Develop and deliver bespoke demonstrations that highlight Snowflake's value proposition, tailored to address specific client requirements pertaining to analytic functionalities. • Demonstrate the ability to elucidate Snowflake's security, privacy, and governance considerations with respect to Analytics. • Be the technical expert in the room that positions Snowflake’s analytical features and value to technical stakeholders at Snowflake’s customers across the Americas. • Function as the principal technical authority, articulating Snowflake's analytical functionalities and value proposition to technical stakeholders within customer organizations across the Americas. • Collaborate with Snowflake account teams and customer advocates to define and execute Proof of Concepts, ensuring successful outcomes and demonstrable technical achievements that validate Snowflake's capabilities, including comprehensive executive summaries and business value assessments. • Develop and disseminate content to facilitate team and organizational growth, such as blog articles, conference presentations, and technical materials including notebooks and demonstrations. • Act as the Voice of Customer by triaging Product gaps and communicating their priority to Product Management. • Develop sales programs with Product Marketing around GTM for Analytics features.