Senior Data Engineer

Nimble Gravity
LATAM +1 more
Remote

Who this role is best for

Geared toward data engineers comfortable with cloud data pipeline design and AI-ready data architecture, who have experience in hybrid environments and a coaching leadership style. The role is remote-friendly and emphasizes collaboration across decentralized teams.

Best fit for

  • Candidates with 5–7 years of data engineering experience and a background in Snowflake and semantic modeling
    — “5–7 years of dedicated data engineering experience”
  • Individuals who have led small teams and mentored analysts in engineering best practices
    — “lead a small data engineering team and serve as a technical mentor”
  • Professionals who can design cloud-native pipelines and support AI and natural language data interfaces
    — “ensure our data platform is 'AI-ready' by building the high-quality pipelines”
  • Candidates with strong Python and SQL skills, especially in cloud contexts like Azure Functions
    — “Strong Python skills, particularly in cloud environments”

Things to consider

  • The role requires a collaborative leadership style, not just technical expertise
    — “collaborative, coaching mindset with a passion for teaching”
  • No visa sponsorship available, which may limit eligibility for international candidates
    — “We do not sponsor H1B visas”

How to stand out

  • Highlight experience in transitioning data ecosystems to cloud platforms like Snowflake
    — “support the transition of our data ecosystem from legacy on-prem architecture”
  • Emphasize your ability to design reusable pipeline patterns and semantic layers
    — “design reference architectures and reusable pipeline patterns”
  • Showcase your mentorship experience or willingness to coach decentralized analysts
    — “serve as a technical mentor to decentralized analysts”
  • Demonstrate hands-on Snowflake expertise, especially in performance tuning and data sharing
    — “Advanced, hands-on experience with Snowflake architecture, performance tuning”
  • Include examples of semantic layer work or AI integration in your data pipeline projects
    — “Experience building or maintaining semantic layers”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Designed and implemented scalable data pipelines
  • Led a small data engineering team
  • Enabled decentralized teams to build high-quality data products
Typical background
5-7 years of data engineering experienceAdvanced Snowflake expertiseProven experience in data integration

Skills & requirements

Required

Data EngineeringSnowflakeBatch ProcessingEvent-driven ArchitectureStreaming DataData MeshData PipelinesSemantic LayersdbtData TransformationCloud Data ToolsData SharingPythonSQL

Preferred

AWSAzureAzure FunctionsStream AnalyticsData IntegrationProtocol ParsingMetrics PublishingContinuous Packet CaptureTime Series DB

Stack & domain

SnowflakePythonSQLdbtAzure FunctionsAzure Data FactoryAWSLeadershipMentoringCollaborationTeachingData EngineeringCloud Data PlatformsAISelf-service Data Products

About the role

Original posting from Nimble Gravity via Greenhouse

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

Source: Nimble Gravity careers (Greenhouse)

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