Senior Data Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 5d ago
The date the source published, not the day we noticed it (2026-10-05). Last seen at its source 2h ago.
We have tracked this listing since 9 Oct 2026 (1 days). The employer's own board has carried it every time we have read it, most recently 2 hours ago.
Is it remote?
LATAM (Remote), US (Remote)
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
LATAM, United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $6,500–7,250/mo
Middle 50% of 29 listings that do state pay — Engineering · Senior · LATAM · USD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Greenhouse employer's own board first seen 1d ago · last seen 2h ago
The listing
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