We are seeking a Lead Data Integration Engineer to deliver production dbt and Snowflake models powering GTM analytics and AI agents. You will create trusted semantic layers, unify Salesforce and Gong-style interaction data, and prepare datasets for Snowflake Cortex and Snowflake Intelligence. Responsibilities Design and maintain production data models in dbt and Snowflake to measure account health, engagement velocity, and customer intent signals Integrate structured records with unstructured interaction data (Gong call transcripts, emails, Salesforce CRM objects, and product usage telemetry) into reliable account-level dimensions Define consistent modeling patterns, naming conventions, and metric definitions across GTM datasets Implement and operate an enterprise semantic layer (such as the dbt Semantic Layer or MetricFlow) to prevent conflicting metric calculations across business tools Prepare clean, context-rich datasets optimized for retrieval, search, and reasoning in Snowflake Cortex, Snowflake Intelligence, and internal AI agents Create feature tables and context stores that enable LLM agents for automated meeting prep, competitive intelligence, and account research Administer metadata and data enrichment patterns so AI agents correctly interpret account stages, customer sentiment, and relationship history Develop automated tests, schema validations, and freshness monitors in dbt for both structured metrics and unstructured data transformations Enforce data governance, role-based access controls (RBAC), and audit trails across sensitive sales pipeline and customer interaction data Collaborate with Revenue Operations and governance teams to document data lineage and maintain clear dataset ownership Contribute within an agile sprint cadence, supporting backlog planning, design reviews, and technical standards Deliver self-service data models and documentation so analysts and product teams can query data independently Requirements Proven track record with 5+ years of commercial experience in analytics engineering, data engineering, or data architecture roles Expert-level proficiency in advanced SQL, including window functions, CTEs, complex joins, analytical queries, and execution plan optimization Extensive hands-on experience with 5+ years of production dbt work, including modular project structure, Jinja macros, custom tests, incremental models, and snapshots Solid practical experience with Snowflake, including virtual warehouse sizing, query profile analysis, clustering keys, Time Travel, zero-copy cloning, and access control Demonstrated experience modeling B2B SaaS revenue and interaction data (Salesforce CRM, Gong, Marketo, or subscription billing data) Deep understanding of Kimball dimensional modeling (star schemas, slowly changing dimensions SCD 1/2/4, conformed dimensions) and semantic layer design Daily comfort with Git workflows, branching strategies, code reviews, and automated CI test runs Willingness to use AI-assisted development tools (GitHub Copilot, Cursor, Claude Code) to accelerate modeling work, test coverage, and documentation English proficiency at B2 (Upper-Intermediate) level or higher Nice to have Working familiarity with Snowflake Cortex AI functions (Cortex Search, LLM functions, embeddings) Understanding of vector embeddings, chunking strategies, and retrieval-augmented generation (RAG) for conversational text Working knowledge of Python for data manipulation (Pandas), API extraction, or pipeline automation Experience with metadata and lineage tools such as Alation, Collibra, DataHub, or dbt Docs Background designing self-service data models for Tableau, Looker, or Power BI
