• Design, develop, and optimize scalable dimensional data models and marts using dbt • Build and extend Forward's semantic layer and metrics standards • Help deliver the data foundation that powers AI at Forward • Advance Forward's data centralization vision • Act as the primary liaison with the Data Science team • Collaborate closely with the Data Engineering team • Partner with the Core Technology teams on internal application schema changes • Collaborate with the DevOps team • Elevate team code quality by serving as a technical reviewer • Evaluate, integrate, and operationalize high-value third-party data sources • Champion data governance and quality across the data platform

• 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