AI Data Engineer

MissionJoin the Data Science team as an AI Data Engineer responsible for building the data foundations that make enterprise AI products accurate, explainable, and scalable. This role will design and implement Snowflake and dbt pipelines from raw source data to curated gold-layer datasets, create semantic models that LLM tools can use reliably, and partner with data science, product, and engineering teams to convert data dictionaries and business definitions into AI-ready data products. The ideal candidate is a strong data engineer with deep Snowflake/dbt experience and a practical understanding of how semantic layers, ER relationships, denormalized models, and metadata quality influence LLM and agent performance.Position OverviewSnowflake and dbt engineering: Design, build, optimize, and operate Snowflake pipelines and dbt models across raw, curated, and gold-layer datasets.AI-ready semantic modeling: Create semantic models, relationships, metrics, dimensions, and curated views that allow LLM tools and agents to answer questions accurately.Data dictionary-driven delivery: Translate team-defined data dictionaries, business definitions, and source mappings into tested, governed, and reusable data products.Agent consumption focus: Design datasets for AI agents, natural-language analytics, Snowflake Cortex Analyst, and other LLM-powered tools.Enterprise data modeling: Balance normalized source models, ER relationships, dimensional models, denormalized consumption layers, and semantic-layer needs.Key ResponsibilitiesSnowflake, dbt, and Data Pipeline DevelopmentBuild reliable data pipelines from raw source data through curated silver layers and business-ready gold layers using Snowflake and dbt.Develop modular dbt models, tests, documentation, exposures, and lineage-friendly transformation patterns.Implement incremental processing, snapshots, audit columns, reconciliation, data quality checks, and restartable pipeline patterns.Optimize Snowflake SQL and dbt workloads for performance, scalability, cost, and maintainability.Work with orchestration and DevOps/SRE teams to support CI/CD, environment promotion, pipeline monitoring, and operational runbooks.Semantic Models and AI-Ready Data ProductsCreate Snowflake semantic models and curated views that support accurate natural-language querying through Snowflake Cortex Analyst and related LLM tools.Translate approved data dictionaries into semantic model dimensions, facts, metrics, synonyms, descriptions, relationships, and business rules.Design ER relationships and join paths that are explicit, accurate, and easy for semantic-layer tools and AI agents to use.Create denormalized or consumption-optimized models where appropriate to reduce ambiguity and improve LLM answer quality.Partner with AI developers to understand tool schema needs, agent workflows, and how data model design affects LLM tool performance.Data Modeling, Integration, and ConsolidationDesign logical and physical models that support enterprise data consolidation, analytical reporting, AI workflows, and business operations.Work across source systems, files, APIs, cloud storage, operational systems, and analytical platforms to integrate data into Snowflake.Create reusable patterns for source-to-target mapping, schema evolution, master/reference data alignment, and data product publishing.Collaborate with business and technical stakeholders to validate data definitions, grain, relationships, hierarchies, and measures.Support data consolidation across Integrichain by rationalizing overlapping datasets and aligning enterprise definitions.Snowflake Cortex and AI Platform EnablementUnderstand Snowflake Cortex capabilities, including Cortex Analyst, Cortex Complete, semantic views/models, and metadata-driven AI workflows.Prepare data models and semantic layers for accurate LLM usage, including clear naming, descriptions, relationships, metrics, and governance metadata.Support AI Explorer and similar applications by ensuring curated datasets are reliable, performant, explainable, and governed.Partner with AI and application teams to troubleshoot semantic model issues, poor AI answers, ambiguous joins, missing metadata, or incorrect measures.Contribute to standards for AI-ready data design, semantic model review, data dictionary alignment, and LLM-friendly data modeling.