Senior Data Engineer

- Semantic & AI Context: Experience building or maintaining semantic layers (e.g., dbt Semantic Layer, Snowflake Cortex/Semantic definitions) and structuring data specifically for consumption by downstream AI, LLM, or natural language search features. - Software Skills: Strong Python skills, particularly in cloud environments (e.g., writing Azure Functions for data workloads) and strong SQL capabilities. - Frameworks & Clouds: Exposure to dbt is highly preferred. Experience with Azure data tools (Azure Data Factory) is a strong plus; familiarity with AWS data ecosystems is nice to have.

- Leadership & Mentorship: Provide direct management and career mentorship to one data engineer (with room to grow). Act as a guide for self-taught domain data analysts to mature their engineering practices. - Modernization & Tooling: Drive the adoption of dbt for data transformation and explore the integration of AWS and Azure cloud data tools (ADF, Azure Functions, Stream Analytics) to optimize data flow. Qualifications & Experience

- Software Skills: Strong Python skills, particularly in cloud environments (e.g., writing Azure Functions for data workloads) and strong SQL capabilities. - Frameworks & Clouds: Exposure to dbt is highly preferred. Experience with Azure data tools (Azure Data Factory) is a strong plus; familiarity with AWS data ecosystems is nice to have. - Leadership Style: A collaborative, coaching mindset with a passion for teaching, establishing governance, and raising the technical bar for cross-functional teams.

- Snowflake Platform Ownership: Serve as the core developer for our Snowflake data platform, ensuring optimal performance, modeling, and storage structures. - AI Readiness & Semantic Modeling: Architect and build transform pipelines that deliver high-quality data inputs to Snowflake and dbt AI features. Design and maintain the robust semantic layers and metadata models required to power "Self-Service 2.0" natural language user interfaces. - Data Mesh Enablement: Establish robust reference architectures, standard templates, and CI/CD best practices to enable decentralized line-of-business teams to build safely.

- Data Integration: Proven experience moving data across hybrid environments (on-prem to cloud) utilizing batch, streaming (e.g., Stream Analytics), and event-driven patterns. - Semantic & AI Context: Experience building or maintaining semantic layers (e.g., dbt Semantic Layer, Snowflake Cortex/Semantic definitions) and structuring data specifically for consumption by downstream AI, LLM, or natural language search features. - Software Skills: Strong Python skills, particularly in cloud environments (e.g., writing Azure Functions for data workloads) and strong SQL capabilities.

Position Overview As a Senior Data Engineer, you will architect and build the foundational data pipeline infrastructure that powers our decentralized Data Mesh strategy. In this role, you will support the transition of our data ecosystem from legacy on-prem architecture to a modern cloud data platform leveraging Snowflake. You will design reference architectures and reusable pipeline patterns that support batch, event-based, and streaming data movement. Crucially, you will ensure our data platform is "AI-ready" by building the high-quality pipelines and robust semantic layers needed to drive our next-generation "Self-Service 2.0" natural language data capabilities. Beyond technical execution, you will lead a small data engineering team and serve as a technical mentor to decentralized analysts across the business, empowering them to build high-quality, self-service data products.

- Pipeline Architecture: Design, build, and maintain scalable data pipelines supporting batch, real-time streaming, and event-driven data movement from on-prem and cloud sources. - Snowflake Platform Ownership: Serve as the core developer for our Snowflake data platform, ensuring optimal performance, modeling, and storage structures. - AI Readiness & Semantic Modeling: Architect and build transform pipelines that deliver high-quality data inputs to Snowflake and dbt AI features. Design and maintain the robust semantic layers and metadata models required to power "Self-Service 2.0" natural language user interfaces.

- Experience: 5–7 years of dedicated data engineering experience, with a proven track record of building production-grade data pipelines. - Snowflake Expertise: Advanced, hands-on experience with Snowflake architecture, performance tuning, and data sharing is a strict requirement. - Data Integration: Proven experience moving data across hybrid environments (on-prem to cloud) utilizing batch, streaming (e.g., Stream Analytics), and event-driven patterns.