Senior Data Engineer

Nimble Gravity

. Architect and build foundational data pipeline infrastructure for a decentralized Data Mesh strategy .

Posted 10/5/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining scalable data pipelines, particularly within Snowflake architecture, while mentoring cross-functional teams and driving the adoption of modern data practices. Proficient in transforming data for AI applications and ensuring effective data governance.

Highest-signal resume keywords

Snowflake ArchitectureData Pipeline DevelopmentPython ProgrammingSQL ProficiencyDbt Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringPerformance TuningData MigrationBatch ProcessingReal-Time StreamingEvent-Driven PipelinesSemantic Layer DevelopmentData Structuring for AICloud Data PlatformsData Governance

Soft Skills

Collaborative MindsetCoachingMentoringTeachingCross-Functional Collaboration

Tools & Technologies

SnowflakeDbtAzure Data FactoryAWS Data EcosystemCI/CD Practices

Industry Keywords

Data MeshHybrid EnvironmentsNatural Language InterfacesData SharingProduction-Grade Pipelines

Tech Stack

Tools & technologies

AWSAzureCloudPythonSQL

About the role

Key responsibilities & impact

Architect and build foundational data pipeline infrastructure for a decentralized Data Mesh strategy
Support migration from legacy on-premises architecture to a modern Snowflake cloud data platform
Design, build, and maintain scalable batch, real-time streaming, and event-driven pipelines
Own core development of the Snowflake data platform, including performance, modeling, and storage structures
Build transformation pipelines and semantic layers supporting Snowflake and dbt AI features and natural-language interfaces
Establish reference architectures, standard templates, and CI/CD practices for decentralized business teams
Directly manage and mentor one data engineer
Mentor domain data analysts in engineering practices
Drive adoption of dbt and explore AWS and Azure data tooling integrations
Collaborate directly with clients, engineers, and AI specialists to turn emerging technology into measurable business outcomes

Requirements

What you’ll need

5–7 years of dedicated data engineering experience
Proven track record of building production-grade data pipelines
Advanced, hands-on experience with Snowflake architecture, performance tuning, and data sharing; strict requirement
Proven experience moving data across hybrid environments from on-premises to cloud using batch, streaming, and event-driven patterns
Experience building or maintaining semantic layers, such as dbt Semantic Layer or Snowflake Cortex/Semantic definitions
Experience structuring data for downstream AI, LLM, or natural language search features
Strong Python skills, particularly in cloud environments
Strong SQL capabilities
Exposure to dbt highly preferred
Experience with Azure data tools, especially Azure Data Factory, a strong plus
Familiarity with AWS data ecosystems nice to have
Collaborative, coaching mindset with passion for teaching, establishing governance, and raising the technical bar for cross-functional teams
Must not require H1B visa sponsorship

Benefits

Comp & perks

Equal Opportunity Employer
Direct work with clients, engineers, and AI specialists
Opportunity to help leading financial institutions and other clients adopt AI
Opportunity to teach, facilitate, influence, and help people embrace new ways of working