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Data Engineers work closely with business stakeholders to understand their goals and identify data-driven strategies to achieve those goals. They design data modeling processes, create algorithms and predictive models to extract the insight the business needs and help analyze the data to increase the productivity and efficiency of the business.

Responsibilities:

• Build and maintain the dbt transformation layer (staging → intermediate → marts) with dimensional modeling, tests, and documentation, delivering clean, analysis-ready datasets for reporting and analytics.
• Partner with business stakeholders to translate reporting and analytics requirements into well-modeled, certified data definitions and BI-ready datasets.
• Design, build, and maintain Snowflake Semantic Views — defining the tables, relationships, facts, dimensions, and metrics that form the governed, reusable definitions layer on top of the warehouse.
• Author and curate verified queries, synonyms, and metric definitions so semantic views reliably power natural-language and AI-driven querying (e.g., Cortex Analyst / MCP-based access).
• Use AI/LLM tools (e.g., Claude, Copilot, or similar) as part of the daily engineering workflow — for code generation, query drafting, documentation, and accelerating model development.
• Own data quality, testing, and documentation for your models, and support the governance and certification of datasets and semantic views for broad self-service use.
• Enable BI and downstream consumers (Looker/Tableau) by exposing consistent, trustworthy metrics through the modeled and semantic layers.Skills:

Skills:

• Hands-on production experience building analytics models with dbt (staging/marts patterns, tests, documentation).
• Strong dimensional and semantic data modeling skills — star schemas, facts vs. dimensions, grain, and slowly changing dimensions.
• Production experience with Snowflake, including building or maintaining semantic views / semantic models (or a comparable semantic/metrics layer such as LookML, dbt Semantic Layer/MetricFlow, or Cube).
• Ability to translate business metric definitions into governed, reusable data models and semantic objects.
• Git-based version control and code review workflows for analytics code.
• Strong SQL skills and hands-on Python experience for data transformation, testing, and tooling.
• Practical, working knowledge of LLMs/generative AI — how they're prompted, their capabilities and limitations — and comfort using AI coding/analytics assistants as everyday tools.