Nimble Gravity - Senior Data Engineer
Requirements
• 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. • Snowflake Expertise: • Data Integration: Proven experience moving data across hybrid environments (on-prem to cloud) utilizing batch, streaming (e.g., Stream Analytics), and event-driven patterns. • Data Integration: • 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. • Semantic & AI Context: • 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. • Frameworks & Clouds: • Leadership Style: A collaborative, coaching mindset with a passion for teaching, establishing governance, and raising the technical bar for cross-functional teams. • Leadership Style: • Why Join Nimble Gravity? You'll help leading financial institutions and other clients adopt AI in meaningful ways. You'll work directly with clients, engineers, and AI specialists to turn emerging technology into measurable business outcomes. If you enjoy teaching, facilitating, influencing, and helping people embrace new ways of working, we'd love to talk.
Responsibilities
• 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. • Pipeline Architecture: • Snowflake Platform Ownership: Serve as the core developer for our Snowflake data platform, ensuring optimal performance, modeling, and storage structures. • Snowflake Platform Ownership: • 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. • AI Readiness & Semantic Modeling: • 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 Mesh Enablement: • 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. • Leadership & Mentorship: • 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. • Modernization & Tooling:
Benefits
• You'll help leading financial institutions and other clients adopt AI in meaningful ways. You'll work directly with clients, engineers, and AI specialists to turn emerging technology into measurable business outcomes. If you enjoy teaching, facilitating, influencing, and helping people embrace new ways of working, we'd love to talk.
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