Senior Data Engineer

Nimble Gravity, LLC; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Nimble Gravity, LLC seeks a Senior Data Engineer to architect and build scalable data pipelines powering our decentralized Data Mesh strategy. You will lead Snowflake platform work, design semantic models for AI readiness, and mentor a small team of data engineers and analysts as we transition to a modern cloud-based data platform.

You will drive modernization using dbt, integrate Azure and AWS data tools, and ensure high-quality data inputs for AI-enabled self-service features across the

Qualifications

• 5–7 years of dedicated data engineering experience.
• Advanced, hands-on experience with Snowflake architecture, performance tuning, and data sharing.
• Experience moving data across hybrid environments (on-prem to cloud) using batch, streaming, and event-driven patterns.
• Experience building semantic layers (dbt Semantic Layer, Snowflake Cortex/Semantic definitions) for AI/LLM or NLP features.
• Strong Python and SQL skills in cloud environments; familiarity with Azure and AWS data tools.

Responsibilities

• Design, build, and maintain scalable data pipelines for batch, real-time streaming, and event-driven data movement.
• Own Snowflake data platform development, performance tuning, modeling, and storage structures.
• Architect and build transform pipelines delivering high-quality inputs to Snowflake and dbt AI features; maintain semantic layers and metadata models for AI features.
• Establish reference architectures, templates, and CI/CD practices to enable decentralized teams to build safely.
• Provide direct management and mentorship to one data engineer and guide analysts to mature engineering practices.
• Drive modernization and tooling, including adoption of dbt and integration of Azure/AWS data tools to optimize data flow.

Skills

Python

SQL

Cloud environments

Leadership

Mentorship

Data engineering

Tools

Snowflake

dbt

Azure Data Factory

Azure Functions

Stream Analytics

Job description

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.

Key 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.
• 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.
• 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

• 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.
• 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 Style: A collaborative, coaching mindset with a passion for teaching, establishing governance, and raising the technical bar for cross-functional teams.

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.

Nimble Gravity is an Equal Opportunity Employer and considers applicants without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.