Senior Data Integration Engineer

We are seeking a Senior Data Integration Engineer to build production data models and semantic layers that serve both business teams analyzing account health and pipeline velocity and autonomous AI agents running automated meeting preparation, account research, and relationship tracking through Snowflake Cortex and Snowflake Intelligence. Responsibilities Build and maintain production data models in dbt and Snowflake that track account health, engagement velocity, and customer intent signals Combine structured records and unstructured interaction data (Gong call transcripts, emails, Salesforce CRM objects, and product usage telemetry) into clean account-level dimensions Establish consistent modeling patterns, naming conventions, and metric definitions across GTM datasets Build and maintain an enterprise semantic layer (such as the dbt Semantic Layer or MetricFlow) to eliminate conflicting metric calculations across business tools Prepare clean, context-rich datasets structured for retrieval, search, and reasoning within Snowflake Cortex, Snowflake Intelligence, and internal AI agents Design feature tables and context stores that power LLM agents for automated meeting prep, competitive intelligence, and account research Manage metadata and data enrichment patterns so AI agents correctly interpret account stages, customer sentiment, and relationship history Write automated tests, schema validations, and freshness monitors in dbt for both structured metrics and unstructured data transformations Maintain data governance, role-based access controls (RBAC), and audit trails across sensitive sales pipeline and customer interaction data Partner with Revenue Operations and governance teams to document data lineage and maintain clear dataset ownership Work within an agile sprint cadence, contributing to backlog planning, design reviews, and technical standards Deliver self-service data models and documentation that enable analysts and product teams to query data independently Requirements 3+ years of commercial experience in analytics engineering, data engineering, or data architecture roles Proficiency in advanced SQL, including window functions, CTEs, complex joins, analytical queries, and execution plan optimization 3+ years of production dbt experience, including modular project structure, Jinja macros, custom tests, incremental models, and snapshots Practical experience with Snowflake, including virtual warehouse sizing, query profile analysis, clustering keys, Time Travel, zero-copy cloning, and access control Hands-on experience modeling B2B SaaS revenue and interaction data (Salesforce CRM, Gong, Marketo, or subscription billing data) 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 Flexibility to use AI-assisted development tools (GitHub Copilot, Cursor, Claude Code) to speed up modeling work, test coverage, and documentation English proficiency at B2 level or higher Nice to have Practical experience or working familiarity with Snowflake Cortex AI functions (Cortex Search, LLM functions, embeddings) Familiarity with 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 Experience designing data models for self-service consumption in Tableau, Looker, or Power BI