Senior Data Engineer
Nimble Gravity Open At a glance
Senior Data Engineering role at Nimble Gravity. Remote, United States · no salary stated · we estimate $142,325–$192,038 across the United States.
Nimble Gravity has not published a salary for this role. The figure below is our estimate, not theirs.
dataroles summary, based on the employer's posting.
What you'll do
- Build and maintain pipelines that move batch, streaming and event-driven data between on-premises and cloud systems
- Own Snowflake development, shaping data models and storage structures while tuning platform performance
- Create transformation workflows and semantic models that prepare Snowflake data for AI and natural-language features
- Set shared architecture patterns, templates and CI/CD practices so business teams can build data products safely
- Manage and mentor one data engineer, while coaching analysts on stronger engineering practices
What you bring
- Five to seven years building production data pipelines as a data engineer
- Advanced hands-on Snowflake experience, including architecture, performance tuning and data sharing
- Experience transferring data across on-premises and cloud environments using batch, streaming or event-based approaches
- Strong Python and SQL skills, plus experience creating semantic layers or structuring data for AI and natural-language use
Who this fits
This senior role suits someone who can own Snowflake and pipeline architecture while coaching analysts and managing one data engineer. The position is remote in the United States and does not offer H-1B visa sponsorship.
From the employer
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.
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.
We do not sponsor H1B visas
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