• Meet with clients to understand their technical challenges, goals, and requirements. • Develop customized technical solutions, often involving software, data and AI, or cloud services to meet specific client needs. • Act as a communication bridge between clients and internal engineering, development, and support teams. • Create technical documentation, design documents, training materials, and proof of concepts for presentations. • Drive clarity across stakeholders through consultation, creation, and discussion of architecture work products. • Collaborate with enterprise and solution architects to define solution patterns and reference architectures. • Partner with business stakeholders to understand end‑to‑end processes, decisions, and information flows, and translate them into automated workflows. • Design workflow diagrams and process specifications that clearly define steps, inputs/outputs, and where human vs. automated vs. AI‑driven decisions occur. • Collaborate with data and engineering teams to define the data required for each action or decision and curate it into a well‑modeled Snowflake “data substrate” (tables, views, and data contracts). • Work with data dictionaries/catalogs to ensure every workflow step has clear, reliable data sources and that changes to the data model are reflected in automations. • Identify where AI capabilities (LLMs, classification, extraction, summarization, retrieval, etc.) can safely and effectively augment or replace manual work in workflows. • Monitor and troubleshoot workflow runs, data issues, and AI‑agent calls; implement improvements to increase robustness, observability, and performance. • Define and maintain metrics and dashboards to track workflow performance, throughput, and error rates using the underlying Snowflake data; gather feedback from users and stakeholders, run experiments, and iterate on workflow and agent designs to continuously improve business outcomes.